\ud83d\udd27 Integration Components Added:<\/strong><\/h3>\n\n\n\n\n- HypergraphRFVisualizer Import<\/strong>: Added module import for the\u00a0hypergraph-rf-visualizer.js<\/a>\u00a0at the top of the HTML file<\/li>\n\n\n\n
- Enhanced Hypergraph Panel<\/strong>: The existing hypergraph panel now has comprehensive controls for the transparent overlay functionality<\/li>\n\n\n\n
- Transparent Overlay Layer<\/strong>:\n
\n- Creates a transparent container that overlays the Cesium globe<\/li>\n\n\n\n
- Initializes the HypergraphRFVisualizer with transparent background<\/li>\n\n\n\n
- Positions the 3D hypergraph visualization on top of the globe<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- RF Hypergraph API Integration<\/strong>:\n
\nconnectToHypergraphAPI()<\/code>\u00a0– Connects to the RF hypergraph API at\u00a0localhost:8000<\/code><\/li>\n\n\n\nfetchHypergraphData()<\/code>\u00a0– Retrieves hypergraph data from the API<\/li>\n\n\n\n- Data transformation from API format to visualizer format<\/li>\n\n\n\n
- Real-time status monitoring and updates<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Interactive Controls<\/strong>:\n
\n- Connect\/Disconnect<\/strong>\u00a0to RF hypergraph API<\/li>\n\n\n\n
- Start\/Stop<\/strong>\u00a0hypergraph overlay<\/li>\n\n\n\n
- Show\/Hide<\/strong>\u00a0higher-order hyperedges<\/li>\n\n\n\n
- Toggle<\/strong>\u00a0node labels and frequency-based coloring<\/li>\n\n\n\n
- Opacity slider<\/strong>\u00a0for hyperedge transparency<\/li>\n\n\n\n
- Update interval<\/strong>\u00a0control for real-time data refresh<\/li>\n\n\n\n
- Generate test data<\/strong>\u00a0for demonstration<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Real-time Statistics<\/strong>:\n
\n- Total RF nodes count<\/li>\n\n\n\n
- Total hyperedges count<\/li>\n\n\n\n
- Average hyperedge cardinality<\/li>\n\n\n\n
- Frequency range display<\/li>\n\n\n\n
- Frequency band breakdown (VHF, UHF, SHF)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n
\ud83c\udfaf Key Features:<\/strong><\/h3>\n\n\n\n\n- Transparent Overlay<\/strong>: The hypergraph visualizer renders transparently over the Cesium 3D globe<\/li>\n\n\n\n
- Geolocated Signals<\/strong>: RF signal nodes are positioned based on geographic coordinates<\/li>\n\n\n\n
- Higher-Order Relationships<\/strong>: Visualizes complex hyperedges (3+ nodes) as convex hulls or dashed line groups<\/li>\n\n\n\n
- Frequency Color Coding<\/strong>: Nodes colored by frequency bands for easy identification<\/li>\n\n\n\n
- Real-time Updates<\/strong>: Configurable update intervals for live data from the RF hypergraph API<\/li>\n\n\n\n
- Power-based Scaling<\/strong>: Node sizes scale based on signal strength<\/li>\n\n\n\n
- Interactive Controls<\/strong>: Full control panel for customizing the visualization<\/li>\n<\/ul>\n\n\n\n
\ud83c\udf10 Integration with Cesium Globe:<\/strong><\/h3>\n\n\n\nThe hypergraph overlay seamlessly integrates with the existing Cesium visualization by:<\/p>\n\n\n\n
\n- Using absolute positioning to overlay on the 3D globe<\/li>\n\n\n\n
- Maintaining transparency to show both hypergraph and geographic data<\/li>\n\n\n\n
- Coordinating with the existing tab system and UI controls<\/li>\n\n\n\n
- Providing real-time updates without interfering with other visualizations<\/li>\n<\/ul>\n\n\n\n
This creates a powerful “Transparent Overlay Layer Hypergraph Mapping of Geolocated Signals on the 3D Cesium Globe” that combines the RF signal analysis capabilities from rf_hypergraph_api.py<\/code> with the sophisticated 3D visualization from hypergraph-rf-visualizer.js<\/a>, all transparently overlaid on the interactive 3D globe in command-ops-visualization.html<\/a>.<\/p>\n\n\n\nThe integration allows users to visualize complex RF signal relationships in real-time while maintaining full geographic context and interaction with the underlying Cesium globe visualization.<\/p>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n\n\n\n
Compelling real-world example of how RF hypergraphs are being applied to predict anomalies and optimize performance<\/strong> in complex systems:<\/p>\n\n\n\n
\n\n\n\n\ud83d\udef0\ufe0f Real-World Application: RF Hypergraphs in Satellite Communication Networks<\/strong><\/h3>\n\n\n\nIn satellite systems\u2014especially those involving phased array antennas<\/strong> and beamforming<\/strong>\u2014RF hypergraphs are used to model and monitor the multi-frequency, multi-node interactions<\/strong> between:<\/p>\n\n\n\n\n- Transmitters and receivers<\/li>\n\n\n\n
- Ground stations and satellites<\/li>\n\n\n\n
- Frequency bands and modulation schemes<\/li>\n<\/ul>\n\n\n\n
Satellite networks operate across shared RF spectra<\/strong>, where interference, signal degradation, or misalignment can cause:<\/p>\n\n\n\n\ud83d\udce1 The Problem<\/h4>\n\n\n\n![\"\"](\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:1024\/h:980\/q:mauto\/f:best\/http:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/06\/Fizzs-Arch-Nemesis-Samsung.png\")
<\/figure>\n\n\n\n\n- Loss of data integrity<\/li>\n\n\n\n
- Beam misdirection<\/li>\n\n\n\n
- Power inefficiencies<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n
Traditional graph models fail to capture higher-order interference patterns<\/strong>, such as when three or more nodes<\/strong> (e.g., satellites, ground stations, and relay links) interact simultaneously<\/strong> across overlapping frequency bands.<\/p>\n\n\n\n\ud83e\udde0 The Hypergraph Solution<\/h4>\n\n\n\n
Researchers model the system as an RF hypergraph<\/strong>, where:<\/p>\n\n\n\n\n- Nodes<\/strong> = RF components (e.g., antennas, frequency bands, amplifiers)<\/li>\n\n\n\n
- Hyperedges<\/strong> = Multi-way interactions (e.g., shared spectrum use, beamforming dependencies)<\/li>\n<\/ul>\n\n\n\n
Using hypergraph neural networks (HGNNs)<\/strong>, the system learns:<\/p>\n\n\n\n\n- Normal signal propagation patterns<\/li>\n\n\n\n
- Expected phase and amplitude relationships<\/li>\n\n\n\n
- Typical interference signatures<\/li>\n<\/ul>\n\n\n\n
\ud83d\udea8 Anomaly Detection in Action<\/h4>\n\n\n\n
When a solar flare<\/strong> or hardware fault<\/strong> disrupts the RF environment:<\/p>\n\n\n\n\n- The hypergraph model detects nonlinear deviations<\/strong> in signal relationships<\/li>\n\n\n\n
- It flags hyperedges<\/strong> with abnormal behavior (e.g., unexpected phase shifts across multiple nodes)<\/li>\n\n\n\n
- Operators receive early warnings to reconfigure beam paths<\/strong> or adjust power levels<\/strong><\/li>\n<\/ul>\n\n\n\n
This approach has been used in military satellite constellations<\/strong> and deep-space communication arrays<\/strong>, where resilience and precision<\/strong> are critical.<\/p>\n\n\n\n
\n\n\n\n\ud83e\uddec Broader Implications<\/h3>\n\n\n\n
This same technique is being explored in:<\/p>\n\n\n\n
\n- Particle accelerators<\/strong> (like the LHC) to detect RF cavity breakdowns<\/li>\n\n\n\n
- 5G\/6G networks<\/strong> to manage dynamic spectrum allocation<\/li>\n\n\n\n
- Quantum computing<\/strong> to model entangled qubit interactions<\/li>\n<\/ul>\n\n\n\n
For a deeper dive into the structural patterns and mining tools used in real-world hypergraphs, check out the CIKM 2022 tutorial on hypergraph mining<\/a>.<\/p>\n\n\n\nhttp:\/\/dmlab.kaist.ac.kr\/~kijungs\/papers\/tutorialCIKM2022.pdf<\/p>\n\n\n\n
Mining of Real-world Hypergraphs: Patterns, Tools, and
Generators
Geon Lee
KAIST
Seoul, South Korea
geonlee0325@kaist.ac.kr
Jaemin Yoo
Carnegie Mellon University
Pittsburgh, PA, USA
jaeminyoo@cmu.edu
Kijung Shin
KAIST
Seoul, South Korea
kijungs@kaist.ac.kr
ABSTRACT
Group interactions are prevalent in various complex systems (e.g.,
collaborations of researchers and group discussions on online Q&A
sites), and they are commonly modeled as hypergraphs. Hyperedges, which compose a hypergraph, are non-empty subsets of any
number of nodes, and thus each hyperedge naturally represents
a group interaction among entities. The higher-order nature of
hypergraphs brings about unique structural properties that have
not been considered in ordinary pairwise graphs.
In this tutorial, we offer a comprehensive overview of a new
research topic called hypergraph mining. We first present recently
revealed structural properties of real-world hypergraphs, including
(a) static and dynamic patterns, (b) global and local patterns, and (c)
connectivity and overlapping patterns. Together with the patterns,
we describe advanced data mining tools used for their discovery.
Lastly, we introduce simple yet realistic hypergraph generative
models that provide an explanation of the structural properties.
CCS CONCEPTS<\/p>\n\n\n\n
\n- Information systems \u2192 Social networks; Data mining.
KEYWORDS
hypergraphs, social networks, structure mining, graph generators
ACM Reference Format:
Geon Lee, Jaemin Yoo, and Kijung Shin. 2022. Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. In Proceedings of the 31st ACM
International Conference on Information and Knowledge Management (CIKM
\u201922), October 17\u201321, 2022, Atlanta, GA, USA. ACM, New York, NY, USA,
4 pages. https:\/\/doi.org\/10.1145\/3511808.3557505
1 BASIC INFORMATION<\/li>\n\n\n\n - The tutorial is half-day long, i.e., 3 hours with breaks.<\/li>\n\n\n\n
- The slides for this tutorial are available at https:\/\/sites.google.
com\/view\/hypergraph-tutorial-cikm.
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and\/or a
fee. Request permissions from permissions@acm.org.
CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA
\u00a9 2022 Association for Computing Machinery.
ACM ISBN 978-1-4503-9236-5\/22\/10. . . $15.00
https:\/\/doi.org\/10.1145\/3511808.3557505
Figure 1: The co-authorship among five authors in four publications [7, 19\u201321] are represented as a hypergraph with five
nodes and four hyperedges.
2 IMPORTANCE AND RELEVANCE
Group interactions are omnipresent in real-world complex systems:
collaborations of researchers, joint interactions of proteins, copurchases of items, to name a few. Such group interactions among
entities are commonly modeled as a hypergraph, which consists of
nodes and hyperedges (see Figure 1 for an example). A hyperedge,
which is a non-empty subset of nodes, naturally models a group
interaction among any number of entities. Thanks to the powerful
expressiveness of hypergraphs, they have been used in a wide
range of fields, including recommender systems [22, 30], computer
vision [17, 27], natural language processing [9, 12], social network
analysis [28], bioinformatics [14], and circuit designs [16].
Motivated by the successful investigation of structural patterns
in real-world pairwise graphs (e.g., power-law degree distribution [1, 11], six degrees of separation [13, 15], and network motifs [24, 25]) and their wide range of applications, such patterns in
real-world hypergraphs have been extensively studied recently. The
flexibility in the size of each hyperedge, which provides the expressiveness of hypergraphs, brings about unique structural properties
that have not been considered in pairwise graphs, and specialized
tools have been developed to analyze their structural patterns. Moreover, several efforts have been made to reproduce and thus explain
the patterns through intuitive hypergraph generative models.
In this half-day tutorial, we provide a comprehensive overview of
structural patterns discovered in real-world hypergraphs, advanced
data mining tools for hypergraphs, and hypergraph generative
models based on the patterns.
While this topic hypergraph mining is in its infant stage, we
believe it will be of interest of a much larger group of researchers,
CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA Geon Lee, Jaemin Yoo, & Kijung Shin<\/li>\n\n\n\n - Part I: Introduction
\u25e6 Group interactions in the real-world
\u25e6 Power of hypergraph modeling [26, 29]
\u25e6 Data repositories and open-source software for hypergraph mining<\/li>\n\n\n\n - Part II: Static Structural Patterns in Hypergraphs and Data Mining Tools for Their Discovery
\u25e6 Basic patterns
\u22c4 Node-level properties [10, 18, 19]
\u22c4 Hyperedge-level properties [18, 19]
\u22c4 Hypergraph-level properties [2, 10, 18]
\u25e6 Advanced patterns (sub-hypergraph-level properties) [2, 19, 20, 23]<\/li>\n\n\n\n - Part III: Dynamic Structural Patterns in Hypergraphs and Data Mining Tools for Their Discovery
\u25e6 Basic patterns
\u22c4 Node-level properties [3, 7]
\u22c4 Hyperedge-level properties [3, 4, 21]
\u22c4 Hypergraph-level properties [18]
\u25e6 Advanced patterns (sub-hypergraph-level properties) [2, 8, 21]<\/li>\n\n\n\n - Part IV: Generative Models of Hypergraphs
\u25e6 Full-hypergraph generation
\u22c4 Static hypergraphs [5, 19]
\u22c4 Dynamic hypergraphs [10, 18]
\u25e6 Sub-hypergraph generation
\u22c4 Static sub-hypergraphs [6]
\u22c4 Dynamic sub-hypergraphs [3, 8]
Figure 2: The brief outline of the proposed tutorial.
especially those interested in graphs, when considering the representational power, usability, and omnipresence of hypergraphs.
Moreover, patterns and generative models of hypergraph data will
have a huge impact on our understanding of complex systems and
also on various applications, including algorithm design, simulation, and anomaly detection, as those of graph data do. This tutorial
aims to provide a starting point for further studies on this topic.
Relevance to CIKM: This tutorial on novel and interdisciplinary
directions covers various aspects of data mining, including findings,
algorithms, and applications. It should be noticed that more than
half of the studies covered in the tutorial appeared in conferences
in data mining (specifically, ICDM, KDD, WWW, VLDB, and SDM).
3 TARGET AUDIENCE AND PREREQUISITES
This tutorial is targeted at anyone interested in graph mining,
graph learning, social network analysis, or network science, from
researchers to the practitioners from industry. It should be noticed
that hypergraphs have been used for modeling data from a variety
of domains, including recommender systems [22, 30], computer
vision [17, 27], natural language processing [9, 12], social network
analysis [28], and thus they are of interest to practitioners.
Basic knowledge of linear algebra and probability theory will
be helpful. For the audience new to this field, we will cover all
necessary preliminaries and provide an intuitive overview of recent
studies on the topic. We will also offer in-depth descriptions of
advanced techniques for the audience with more experience in
this field. Specifically, the audience of this tutorial will be able
to (1) understand the basic hypergraph-related concepts, (2) use
the concepts to model and analyze group interactions in various
real-world complex systems, and (3) understand structural design
principles of real-world hypergraphs.
4 OUTLINE AND CONTENTS
We provide a brief outline of the tutorial in Figure 2.
In this tutorial, we focus on providing a comprehensive overview
of structural patterns discovered in real-world hypergraphs, and
advanced data mining tools for large-scale hypergraphs. As an introduction, we present how hypergraphs are used to model various
types of data from different domains.
During the first half of this tutorial, we introduce structural patterns pervasive in real-world hypergraphs. Specifically, we cover (a)
static structural patterns1
[2, 10, 18\u201320, 23] and (b) dynamic structural patterns2
[2, 3, 7, 8, 18, 21] of real-world hypergraphs where
the static patterns are further divided into (a) node-level patterns,
(b) hyperedge-level patterns, (c) sub-hypergraph-level patterns, and
(d) hypergraph-level patterns, as summarized in Table 1. The presented patterns include macroscopic (i.e., global) [10, 18, 19] and
microscopic (i.e., local) [2, 20, 21, 23] patterns, and they also include
patterns regarding connectivity [10, 18, 23], overlap [2, 8, 19\u201321],
and repetition [3, 4, 7] of hyperedges. Together with the patterns,
we present advanced data mining tools (e.g., hypergraph motifs
[20, 21], multi-level decomposition [10], and a principled measure
of \u201coverlapness\u201d [19]) developed for their discovery.
During the second half, we present generative models of hypergraphs, which are based on the observations made in real-world
hypergraphs. They aim to reproduce and thus explain the structural patterns through intuitive mechanisms on individual nodes or
hyperedges. These models can also be used for creating large-scale
benchmark datasets, for anonymizing hypergraphs with sensitive
information, and for comparing hypergraphs of different sizes. As
categorized in Table 2, we cover four models for generating entire
1Patterns from static hypergraphs or a few snapshots.
2Patterns related to the dynamics of evolving hypergraphs.
Mining of Real-world Hypergraphs: Patterns, Tools, and Generators CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA
Table 1: Categorization of structural properties in real-world hypergraphs that are covered in this tutorial.
Static patterns* (Part II) Dynamic patterns** (Part III)
Nodes Hyperedges Sub-hypergraphs Hypergraphs Nodes Hyperedges Sub-hypergraphs Hypergraphs
Benson et al. (PNAS\u201918) [2] \u2713 \u2713 \u2713
Benson et al. (KDD\u201918) [3] \u2713 \u2713
Cencetti et al. (SciRep\u201921) [4] \u2713
Choo and Shin (SDM\u201922) [7] \u2713
Comrie and Kleinberg (ICDM\u201921) [8] \u2713
Do et al. (KDD\u201920) [10] \u2713 \u2713
Kook et al. (ICDM\u201920) [18] \u2713 \u2713 \u2713 \u2713
Lee et al. (WWW\u201921) [19] \u2713 \u2713 \u2713
Lee et al. (VLDB\u201920) [20] \u2713
Lee and Shin (ICDM\u201921) [21] \u2713 \u2713
Lotito et al (CommsPhys\u201922) [23] \u2713
*Patterns from static hypergraphs or a few snapshots. **Patterns related to the dynamics of evolving hypergraphs.
Table 2: Comparison of the hypergraph generative models
covered in this tutorial.
Full hypergraphs Sub-hypergraphs
Static Dynamic Static Dynamic
Benson et al. (KDD\u201918) [3] \u2713
Comrie and Kleinberg (ICDM\u201921) [8] \u2713
Chodrow (J. Complex Netw\u201920) [5] \u2713
Choe et al (WWW\u201922) [6] \u2713
Do et al. (KDD\u201920) [10] \u2713
Kook et al. (ICDM\u201920) [18] \u2713
Lee et al. (WWW\u201921) [19] \u2713
hypergraphs models [5, 10, 18, 19] and two models for generating
sub-hypergraphs [3, 6, 8]. In addition to their technical details, we
present how realistic they are in various aspects.
5 IMPORTANT REFERENCES
The important references covered in the tutorial are provided below.<\/li>\n\n\n\n - [PNAS\u201918] Austin R Benson, Rediet Abebe, Michael T Schaub, Ali
Jadbabaie, and Jon Kleinberg. Simplicial closure and higher-order
link prediction. PNAS, 115(48):E11221\u2013E11230, 2018.<\/li>\n\n\n\n - [KDD\u201918] Austin R Benson, Ravi Kumar, and Andrew Tomkins.
Sequences of sets. In KDD, 2018<\/li>\n\n\n\n - [SciRep\u201921] Giulia Cencetti, Federico Battiston, Bruno Lepri, and
M\u00e1rton Karsai. Temporal properties of higher-order interactions in
social networks. Scientific reports, 11(1):1\u201310, 2021.<\/li>\n\n\n\n - [J. Complex Netw\u201920] Philip S Chodrow. Configuration models of
random hypergraphs. J. Complex Networks, 8(3):cnaa018, 2020.<\/li>\n\n\n\n - [WWW\u201922] Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung
Baek, U Kang, and Kijung Shin. Midas: Representative sampling
from real-world hypergraphs. In WWW, 2022.<\/li>\n\n\n\n - [SDM\u201922] Hyunjin Choo and Kijung Shin. On the persistence of
higher-order interactions in real-world hypergraphs. SDM, 2022.<\/li>\n\n\n\n - [ICDM\u201921a] Cazamere Comrie and Jon Kleinberg. Hypergraph
ego-networks and their temporal evolution. In ICDM, 2021.<\/li>\n\n\n\n - [KDD\u201920] Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin. Structural patterns and generative models of real-world
hypergraphs. In KDD, 2020.<\/li>\n\n\n\n - [ICDM\u201920] Yunbum Kook, Jihoon Ko, and Kijung Shin. Evolution
of real-world hypergraphs: Patterns and models without oracles. In
ICDM, 2020.<\/li>\n\n\n\n - [WWW\u201921] Geon Lee, Minyoung Choe, and Kijung Shin. How do
hyperedges overlap in real-world hypergraphs?-patterns, measures,
and generators. In WWW, 2021.<\/li>\n\n\n\n - [VLDB\u201920] Geon Lee, Jihoon Ko, and Kijung Shin. Hypergraph motifs: concepts, algorithms, and discoveries. PVLDB, 13(12):2256\u20132269,
2020.<\/li>\n\n\n\n - [ICDM\u201921b] Geon Lee and Kijung Shin. THyMe+: Temporal hypergraph motifs and fast algorithms for exact counting. In ICDM,
2021.<\/li>\n\n\n\n - [CommPhys\u201922] Quintino Francesco Lotito, Federico Musciotto,
Alberto Montresor, and Federico Battiston. Higher-order motif
analysis in hypergraphs. Comm. Phys, 5(1):1\u20138, 2022.<\/li>\n\n\n\n - [SIREV\u201921] Leo Torres, Ann S. Blevins, Danielle Bassett, and Tina
Eliassi-Rad. The why, how, and when of representations for complex
systems. SIAM Review 63(3):435-485, 2021
6 RELEVANT TUTORIALS
There have been tutorials on mining of graphs in general, including:<\/li>\n\n\n\n - Graph Structures in Data Mining in KDD 2004
\u2013 http:\/\/www.cs.cmu.edu\/~christos\/TALKS\/KDD04-tut\/
\u2013 This tutorial focuses on (1) topological properties of nodes and
edges, (2) importance measures of nodes, and (3) similarity and
influence between nodes in graphs.<\/li>\n\n\n\n - Large Graph Mining: Patterns, Tools, and Case Studies in CIKM
2008 & ICDE 2009
\u2013 http:\/\/tonghanghang.org\/pdfs\/tut-icde09-part1_patterns.pdf
\u2013 This tutorial focuses on (1) structural patterns, (2) matrix &
tensor tools, (3) proximity measures between nodes, and (4)
case studies of real-world graphs.<\/li>\n\n\n\n - Mining Billion-Scale Graphs: Patterns and Algorithms in SIGMOD
2012
\u2013 https:\/\/www.cs.cmu.edu\/~christos\/TALKS\/12-SIGMOD-tutorial\/
\u2013 This tutorial focuses on (1) patterns in real-world graphs, (2)
tools for pattern mining in graphs, and (3) scalable algorithms
for large-scale graphs.<\/li>\n\n\n\n - Advanced Graph Mining for Community Evaluation in Social Networks and the Web in WSDM 2013
\u2013 http:\/\/www.lix.polytechnique.fr\/~mvazirg\/WSDM2013_tutorial
\u2013 This tutorial focuses on detection and evaluation methods of
communities in graphs.<\/li>\n\n\n\n - Big Graph Mining: Algorithms, Anomaly Detection, and Applications in ASONAM 2013
CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA Geon Lee, Jaemin Yoo, & Kijung Shin
\u2013 https:\/\/datalab.snu.ac.kr\/~ukang\/talks\/13-ASONAM-tutorial\/
\u2013 This tutorial focuses on (1) scalable graph mining, (2) graphbased anomaly detection, and (3) applications.<\/li>\n\n\n\n - Core Decomposition of Networks: Concepts, Algorithms and Applications in ICDM 2016 & PKDD 2017
\u2013 https:\/\/fragkiskos.me\/projects\/core_tutorial\/
\u2013 This tutorial focuses on (a) the concept and properties of core
decomposition, (b) efficient computation, and (c) applications.<\/li>\n\n\n\n - Roles in Networks – Foundations, Methods and Applications in
ICDM 2021
\u2013 https:\/\/cswzhang.github.io\/icdm-tutorial-2021\/
\u2013 This tutorial focuses on (a) the taxonomy of role analytic methods, (b) role-based embedding methods, (c) and applications.
To the best of our knowledge, however, no tutorial that focuses on
hypergraphs has been offered in data-mining and related venues.
The patterns, tools and models covered in this tutorial are clearly
distinguished from those for ordinary graphs. We plan to deliver
the same tutorial at DSAA 2022 and ICDM 2022.
7 SHORT BIO OF PRESENTERS
Geon Lee (https:\/\/geonlee0325.github.io) is a Ph.D. student at the
Kim Jaechul Graduate School of AI at KAIST. He received his B.S.
degree in Computer Science and Engineering from Sungkyunkwan
University in 2019. His research interests include graph mining
and its applications. Especially, his studies of hypergraphs have
appeared in major data mining venues, including VLDB, WWW,
and ICDM.
Jaemin Yoo (https:\/\/jaeminyoo.github.io) is a postdoctoral research
fellow in the Heinz College of Information Systems and Public
Policy at Carnegie Mellon University. He received his Ph.D. and
B.S. in Computer Science and Engineering from Seoul National
University. His research interests include probabilistic mining and
machine learning on graphs. His work has been published in major
venues including WWW, KDD, and NeurIPS. He is a recipient of the
Google PhD Fellowship and the Qualcomm Innovation Fellowship.
Kijung Shin (https:\/\/kijungs.github.io\/) is an Ewon Endowed Assistant Professor (jointly affiliated) in the Kim Jaechul Graduate
School of AI and the School of Electrical Engineering at KAIST.
He received his Ph.D. in Computer Science from Carnegie Mellon
University in 2019. He has published more than 50 referred articles
at major data mining venues, and he won the best research paper
award at KDD 2016. His research interests span a wide range of
topics on graph mining, with a focus on scalable algorithm design
and empirical analysis of real-world hypergraphs.
ACKNOWLEDGEMENTS
This work was supported by National Research Foundation of Korea
(NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1C1C1008296) and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the
Korea government (MSIT) (No. 2022-0-00157, Robust, Fair, Extensible Data-Centric Continual Learning).
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[1] Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert. 1999. Emergence of scaling in random
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Double-scale self-supervised hypergraph learning for group recommendation. In
CIKM.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Ben Gilbert 8\/15\/2025 Integration of the RF Hypergraph functionality as a transparent overlay layer on the 3D Cesium Globe. Here’s a summary of what I’ve implemented: RF Hypergraph Transparent Overlay Integration Summary \ud83d\udd27 Integration Components Added: \ud83c\udfaf Key Features: \ud83c\udf10 Integration with Cesium Globe: The hypergraph overlay seamlessly integrates with the existing Cesium visualization by: This creates a… RF Hypergraphs in Satellite Communication Networks<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":426,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-857","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/857","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=857"}],"version-history":[{"count":3,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/857\/revisions"}],"predecessor-version":[{"id":2863,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/857\/revisions\/2863"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/426"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- \n
- Creates a transparent container that overlays the Cesium globe<\/li>\n\n\n\n
- Initializes the HypergraphRFVisualizer with transparent background<\/li>\n\n\n\n
- Positions the 3D hypergraph visualization on top of the globe<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- RF Hypergraph API Integration<\/strong>:\n
- \n
connectToHypergraphAPI()<\/code>\u00a0– Connects to the RF hypergraph API at\u00a0localhost:8000<\/code><\/li>\n\n\n\nfetchHypergraphData()<\/code>\u00a0– Retrieves hypergraph data from the API<\/li>\n\n\n\n- Data transformation from API format to visualizer format<\/li>\n\n\n\n
- Real-time status monitoring and updates<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Interactive Controls<\/strong>:\n
- \n
- Connect\/Disconnect<\/strong>\u00a0to RF hypergraph API<\/li>\n\n\n\n
- Start\/Stop<\/strong>\u00a0hypergraph overlay<\/li>\n\n\n\n
- Show\/Hide<\/strong>\u00a0higher-order hyperedges<\/li>\n\n\n\n
- Toggle<\/strong>\u00a0node labels and frequency-based coloring<\/li>\n\n\n\n
- Opacity slider<\/strong>\u00a0for hyperedge transparency<\/li>\n\n\n\n
- Update interval<\/strong>\u00a0control for real-time data refresh<\/li>\n\n\n\n
- Generate test data<\/strong>\u00a0for demonstration<\/li>\n<\/ul>\n<\/li>\n\n\n\n
- Real-time Statistics<\/strong>:\n
- \n
- Total RF nodes count<\/li>\n\n\n\n
- Total hyperedges count<\/li>\n\n\n\n
- Average hyperedge cardinality<\/li>\n\n\n\n
- Frequency range display<\/li>\n\n\n\n
- Frequency band breakdown (VHF, UHF, SHF)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n
\ud83c\udfaf Key Features:<\/strong><\/h3>\n\n\n\n
- \n
- Transparent Overlay<\/strong>: The hypergraph visualizer renders transparently over the Cesium 3D globe<\/li>\n\n\n\n
- Geolocated Signals<\/strong>: RF signal nodes are positioned based on geographic coordinates<\/li>\n\n\n\n
- Higher-Order Relationships<\/strong>: Visualizes complex hyperedges (3+ nodes) as convex hulls or dashed line groups<\/li>\n\n\n\n
- Frequency Color Coding<\/strong>: Nodes colored by frequency bands for easy identification<\/li>\n\n\n\n
- Real-time Updates<\/strong>: Configurable update intervals for live data from the RF hypergraph API<\/li>\n\n\n\n
- Power-based Scaling<\/strong>: Node sizes scale based on signal strength<\/li>\n\n\n\n
- Interactive Controls<\/strong>: Full control panel for customizing the visualization<\/li>\n<\/ul>\n\n\n\n
\ud83c\udf10 Integration with Cesium Globe:<\/strong><\/h3>\n\n\n\n
The hypergraph overlay seamlessly integrates with the existing Cesium visualization by:<\/p>\n\n\n\n
- \n
- Using absolute positioning to overlay on the 3D globe<\/li>\n\n\n\n
- Maintaining transparency to show both hypergraph and geographic data<\/li>\n\n\n\n
- Coordinating with the existing tab system and UI controls<\/li>\n\n\n\n
- Providing real-time updates without interfering with other visualizations<\/li>\n<\/ul>\n\n\n\n
This creates a powerful “Transparent Overlay Layer Hypergraph Mapping of Geolocated Signals on the 3D Cesium Globe” that combines the RF signal analysis capabilities from
rf_hypergraph_api.py<\/code> with the sophisticated 3D visualization from hypergraph-rf-visualizer.js<\/a>, all transparently overlaid on the interactive 3D globe in command-ops-visualization.html<\/a>.<\/p>\n\n\n\nThe integration allows users to visualize complex RF signal relationships in real-time while maintaining full geographic context and interaction with the underlying Cesium globe visualization.<\/p>\n\n\n\n
<\/p>\n\n\n\n
<\/p>\n\n\n\n
Compelling real-world example of how RF hypergraphs are being applied to predict anomalies and optimize performance<\/strong> in complex systems:<\/p>\n\n\n\n
\n\n\n\n\ud83d\udef0\ufe0f Real-World Application: RF Hypergraphs in Satellite Communication Networks<\/strong><\/h3>\n\n\n\n
In satellite systems\u2014especially those involving phased array antennas<\/strong> and beamforming<\/strong>\u2014RF hypergraphs are used to model and monitor the multi-frequency, multi-node interactions<\/strong> between:<\/p>\n\n\n\n
- \n
- Transmitters and receivers<\/li>\n\n\n\n
- Ground stations and satellites<\/li>\n\n\n\n
- Frequency bands and modulation schemes<\/li>\n<\/ul>\n\n\n\n
Satellite networks operate across shared RF spectra<\/strong>, where interference, signal degradation, or misalignment can cause:<\/p>\n\n\n\n
\ud83d\udce1 The Problem<\/h4>\n\n\n\n
<\/figure>\n\n\n\n- \n
- Loss of data integrity<\/li>\n\n\n\n
- Beam misdirection<\/li>\n\n\n\n
- Power inefficiencies<\/li>\n<\/ul>\n\n\n\n
<\/p>\n\n\n\n
Traditional graph models fail to capture higher-order interference patterns<\/strong>, such as when three or more nodes<\/strong> (e.g., satellites, ground stations, and relay links) interact simultaneously<\/strong> across overlapping frequency bands.<\/p>\n\n\n\n
\ud83e\udde0 The Hypergraph Solution<\/h4>\n\n\n\n
Researchers model the system as an RF hypergraph<\/strong>, where:<\/p>\n\n\n\n
- \n
- Nodes<\/strong> = RF components (e.g., antennas, frequency bands, amplifiers)<\/li>\n\n\n\n
- Hyperedges<\/strong> = Multi-way interactions (e.g., shared spectrum use, beamforming dependencies)<\/li>\n<\/ul>\n\n\n\n
Using hypergraph neural networks (HGNNs)<\/strong>, the system learns:<\/p>\n\n\n\n
- \n
- Normal signal propagation patterns<\/li>\n\n\n\n
- Expected phase and amplitude relationships<\/li>\n\n\n\n
- Typical interference signatures<\/li>\n<\/ul>\n\n\n\n
\ud83d\udea8 Anomaly Detection in Action<\/h4>\n\n\n\n
When a solar flare<\/strong> or hardware fault<\/strong> disrupts the RF environment:<\/p>\n\n\n\n
- \n
- The hypergraph model detects nonlinear deviations<\/strong> in signal relationships<\/li>\n\n\n\n
- It flags hyperedges<\/strong> with abnormal behavior (e.g., unexpected phase shifts across multiple nodes)<\/li>\n\n\n\n
- Operators receive early warnings to reconfigure beam paths<\/strong> or adjust power levels<\/strong><\/li>\n<\/ul>\n\n\n\n
This approach has been used in military satellite constellations<\/strong> and deep-space communication arrays<\/strong>, where resilience and precision<\/strong> are critical.<\/p>\n\n\n\n
\n\n\n\n\ud83e\uddec Broader Implications<\/h3>\n\n\n\n
This same technique is being explored in:<\/p>\n\n\n\n
- \n
- Particle accelerators<\/strong> (like the LHC) to detect RF cavity breakdowns<\/li>\n\n\n\n
- 5G\/6G networks<\/strong> to manage dynamic spectrum allocation<\/li>\n\n\n\n
- Quantum computing<\/strong> to model entangled qubit interactions<\/li>\n<\/ul>\n\n\n\n
For a deeper dive into the structural patterns and mining tools used in real-world hypergraphs, check out the CIKM 2022 tutorial on hypergraph mining<\/a>.<\/p>\n\n\n\n
http:\/\/dmlab.kaist.ac.kr\/~kijungs\/papers\/tutorialCIKM2022.pdf<\/p>\n\n\n\n
Mining of Real-world Hypergraphs: Patterns, Tools, and
Generators
Geon Lee
KAIST
Seoul, South Korea
geonlee0325@kaist.ac.kr
Jaemin Yoo
Carnegie Mellon University
Pittsburgh, PA, USA
jaeminyoo@cmu.edu
Kijung Shin
KAIST
Seoul, South Korea
kijungs@kaist.ac.kr
ABSTRACT
Group interactions are prevalent in various complex systems (e.g.,
collaborations of researchers and group discussions on online Q&A
sites), and they are commonly modeled as hypergraphs. Hyperedges, which compose a hypergraph, are non-empty subsets of any
number of nodes, and thus each hyperedge naturally represents
a group interaction among entities. The higher-order nature of
hypergraphs brings about unique structural properties that have
not been considered in ordinary pairwise graphs.
In this tutorial, we offer a comprehensive overview of a new
research topic called hypergraph mining. We first present recently
revealed structural properties of real-world hypergraphs, including
(a) static and dynamic patterns, (b) global and local patterns, and (c)
connectivity and overlapping patterns. Together with the patterns,
we describe advanced data mining tools used for their discovery.
Lastly, we introduce simple yet realistic hypergraph generative
models that provide an explanation of the structural properties.
CCS CONCEPTS<\/p>\n\n\n\n- \n
- Information systems \u2192 Social networks; Data mining.
KEYWORDS
hypergraphs, social networks, structure mining, graph generators
ACM Reference Format:
Geon Lee, Jaemin Yoo, and Kijung Shin. 2022. Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. In Proceedings of the 31st ACM
International Conference on Information and Knowledge Management (CIKM
\u201922), October 17\u201321, 2022, Atlanta, GA, USA. ACM, New York, NY, USA,
4 pages. https:\/\/doi.org\/10.1145\/3511808.3557505
1 BASIC INFORMATION<\/li>\n\n\n\n - The tutorial is half-day long, i.e., 3 hours with breaks.<\/li>\n\n\n\n
- The slides for this tutorial are available at https:\/\/sites.google.
com\/view\/hypergraph-tutorial-cikm.
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and\/or a
fee. Request permissions from permissions@acm.org.
CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA
\u00a9 2022 Association for Computing Machinery.
ACM ISBN 978-1-4503-9236-5\/22\/10. . . $15.00
https:\/\/doi.org\/10.1145\/3511808.3557505
Figure 1: The co-authorship among five authors in four publications [7, 19\u201321] are represented as a hypergraph with five
nodes and four hyperedges.
2 IMPORTANCE AND RELEVANCE
Group interactions are omnipresent in real-world complex systems:
collaborations of researchers, joint interactions of proteins, copurchases of items, to name a few. Such group interactions among
entities are commonly modeled as a hypergraph, which consists of
nodes and hyperedges (see Figure 1 for an example). A hyperedge,
which is a non-empty subset of nodes, naturally models a group
interaction among any number of entities. Thanks to the powerful
expressiveness of hypergraphs, they have been used in a wide
range of fields, including recommender systems [22, 30], computer
vision [17, 27], natural language processing [9, 12], social network
analysis [28], bioinformatics [14], and circuit designs [16].
Motivated by the successful investigation of structural patterns
in real-world pairwise graphs (e.g., power-law degree distribution [1, 11], six degrees of separation [13, 15], and network motifs [24, 25]) and their wide range of applications, such patterns in
real-world hypergraphs have been extensively studied recently. The
flexibility in the size of each hyperedge, which provides the expressiveness of hypergraphs, brings about unique structural properties
that have not been considered in pairwise graphs, and specialized
tools have been developed to analyze their structural patterns. Moreover, several efforts have been made to reproduce and thus explain
the patterns through intuitive hypergraph generative models.
In this half-day tutorial, we provide a comprehensive overview of
structural patterns discovered in real-world hypergraphs, advanced
data mining tools for hypergraphs, and hypergraph generative
models based on the patterns.
While this topic hypergraph mining is in its infant stage, we
believe it will be of interest of a much larger group of researchers,
CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA Geon Lee, Jaemin Yoo, & Kijung Shin<\/li>\n\n\n\n - Part I: Introduction
\u25e6 Group interactions in the real-world
\u25e6 Power of hypergraph modeling [26, 29]
\u25e6 Data repositories and open-source software for hypergraph mining<\/li>\n\n\n\n - Part II: Static Structural Patterns in Hypergraphs and Data Mining Tools for Their Discovery
\u25e6 Basic patterns
\u22c4 Node-level properties [10, 18, 19]
\u22c4 Hyperedge-level properties [18, 19]
\u22c4 Hypergraph-level properties [2, 10, 18]
\u25e6 Advanced patterns (sub-hypergraph-level properties) [2, 19, 20, 23]<\/li>\n\n\n\n - Part III: Dynamic Structural Patterns in Hypergraphs and Data Mining Tools for Their Discovery
\u25e6 Basic patterns
\u22c4 Node-level properties [3, 7]
\u22c4 Hyperedge-level properties [3, 4, 21]
\u22c4 Hypergraph-level properties [18]
\u25e6 Advanced patterns (sub-hypergraph-level properties) [2, 8, 21]<\/li>\n\n\n\n - Part IV: Generative Models of Hypergraphs
\u25e6 Full-hypergraph generation
\u22c4 Static hypergraphs [5, 19]
\u22c4 Dynamic hypergraphs [10, 18]
\u25e6 Sub-hypergraph generation
\u22c4 Static sub-hypergraphs [6]
\u22c4 Dynamic sub-hypergraphs [3, 8]
Figure 2: The brief outline of the proposed tutorial.
especially those interested in graphs, when considering the representational power, usability, and omnipresence of hypergraphs.
Moreover, patterns and generative models of hypergraph data will
have a huge impact on our understanding of complex systems and
also on various applications, including algorithm design, simulation, and anomaly detection, as those of graph data do. This tutorial
aims to provide a starting point for further studies on this topic.
Relevance to CIKM: This tutorial on novel and interdisciplinary
directions covers various aspects of data mining, including findings,
algorithms, and applications. It should be noticed that more than
half of the studies covered in the tutorial appeared in conferences
in data mining (specifically, ICDM, KDD, WWW, VLDB, and SDM).
3 TARGET AUDIENCE AND PREREQUISITES
This tutorial is targeted at anyone interested in graph mining,
graph learning, social network analysis, or network science, from
researchers to the practitioners from industry. It should be noticed
that hypergraphs have been used for modeling data from a variety
of domains, including recommender systems [22, 30], computer
vision [17, 27], natural language processing [9, 12], social network
analysis [28], and thus they are of interest to practitioners.
Basic knowledge of linear algebra and probability theory will
be helpful. For the audience new to this field, we will cover all
necessary preliminaries and provide an intuitive overview of recent
studies on the topic. We will also offer in-depth descriptions of
advanced techniques for the audience with more experience in
this field. Specifically, the audience of this tutorial will be able
to (1) understand the basic hypergraph-related concepts, (2) use
the concepts to model and analyze group interactions in various
real-world complex systems, and (3) understand structural design
principles of real-world hypergraphs.
4 OUTLINE AND CONTENTS
We provide a brief outline of the tutorial in Figure 2.
In this tutorial, we focus on providing a comprehensive overview
of structural patterns discovered in real-world hypergraphs, and
advanced data mining tools for large-scale hypergraphs. As an introduction, we present how hypergraphs are used to model various
types of data from different domains.
During the first half of this tutorial, we introduce structural patterns pervasive in real-world hypergraphs. Specifically, we cover (a)
static structural patterns1
[2, 10, 18\u201320, 23] and (b) dynamic structural patterns2
[2, 3, 7, 8, 18, 21] of real-world hypergraphs where
the static patterns are further divided into (a) node-level patterns,
(b) hyperedge-level patterns, (c) sub-hypergraph-level patterns, and
(d) hypergraph-level patterns, as summarized in Table 1. The presented patterns include macroscopic (i.e., global) [10, 18, 19] and
microscopic (i.e., local) [2, 20, 21, 23] patterns, and they also include
patterns regarding connectivity [10, 18, 23], overlap [2, 8, 19\u201321],
and repetition [3, 4, 7] of hyperedges. Together with the patterns,
we present advanced data mining tools (e.g., hypergraph motifs
[20, 21], multi-level decomposition [10], and a principled measure
of \u201coverlapness\u201d [19]) developed for their discovery.
During the second half, we present generative models of hypergraphs, which are based on the observations made in real-world
hypergraphs. They aim to reproduce and thus explain the structural patterns through intuitive mechanisms on individual nodes or
hyperedges. These models can also be used for creating large-scale
benchmark datasets, for anonymizing hypergraphs with sensitive
information, and for comparing hypergraphs of different sizes. As
categorized in Table 2, we cover four models for generating entire
1Patterns from static hypergraphs or a few snapshots.
2Patterns related to the dynamics of evolving hypergraphs.
Mining of Real-world Hypergraphs: Patterns, Tools, and Generators CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA
Table 1: Categorization of structural properties in real-world hypergraphs that are covered in this tutorial.
Static patterns* (Part II) Dynamic patterns** (Part III)
Nodes Hyperedges Sub-hypergraphs Hypergraphs Nodes Hyperedges Sub-hypergraphs Hypergraphs
Benson et al. (PNAS\u201918) [2] \u2713 \u2713 \u2713
Benson et al. (KDD\u201918) [3] \u2713 \u2713
Cencetti et al. (SciRep\u201921) [4] \u2713
Choo and Shin (SDM\u201922) [7] \u2713
Comrie and Kleinberg (ICDM\u201921) [8] \u2713
Do et al. (KDD\u201920) [10] \u2713 \u2713
Kook et al. (ICDM\u201920) [18] \u2713 \u2713 \u2713 \u2713
Lee et al. (WWW\u201921) [19] \u2713 \u2713 \u2713
Lee et al. (VLDB\u201920) [20] \u2713
Lee and Shin (ICDM\u201921) [21] \u2713 \u2713
Lotito et al (CommsPhys\u201922) [23] \u2713
*Patterns from static hypergraphs or a few snapshots. **Patterns related to the dynamics of evolving hypergraphs.
Table 2: Comparison of the hypergraph generative models
covered in this tutorial.
Full hypergraphs Sub-hypergraphs
Static Dynamic Static Dynamic
Benson et al. (KDD\u201918) [3] \u2713
Comrie and Kleinberg (ICDM\u201921) [8] \u2713
Chodrow (J. Complex Netw\u201920) [5] \u2713
Choe et al (WWW\u201922) [6] \u2713
Do et al. (KDD\u201920) [10] \u2713
Kook et al. (ICDM\u201920) [18] \u2713
Lee et al. (WWW\u201921) [19] \u2713
hypergraphs models [5, 10, 18, 19] and two models for generating
sub-hypergraphs [3, 6, 8]. In addition to their technical details, we
present how realistic they are in various aspects.
5 IMPORTANT REFERENCES
The important references covered in the tutorial are provided below.<\/li>\n\n\n\n - [PNAS\u201918] Austin R Benson, Rediet Abebe, Michael T Schaub, Ali
Jadbabaie, and Jon Kleinberg. Simplicial closure and higher-order
link prediction. PNAS, 115(48):E11221\u2013E11230, 2018.<\/li>\n\n\n\n - [KDD\u201918] Austin R Benson, Ravi Kumar, and Andrew Tomkins.
Sequences of sets. In KDD, 2018<\/li>\n\n\n\n - [SciRep\u201921] Giulia Cencetti, Federico Battiston, Bruno Lepri, and
M\u00e1rton Karsai. Temporal properties of higher-order interactions in
social networks. Scientific reports, 11(1):1\u201310, 2021.<\/li>\n\n\n\n - [J. Complex Netw\u201920] Philip S Chodrow. Configuration models of
random hypergraphs. J. Complex Networks, 8(3):cnaa018, 2020.<\/li>\n\n\n\n - [WWW\u201922] Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung
Baek, U Kang, and Kijung Shin. Midas: Representative sampling
from real-world hypergraphs. In WWW, 2022.<\/li>\n\n\n\n - [SDM\u201922] Hyunjin Choo and Kijung Shin. On the persistence of
higher-order interactions in real-world hypergraphs. SDM, 2022.<\/li>\n\n\n\n - [ICDM\u201921a] Cazamere Comrie and Jon Kleinberg. Hypergraph
ego-networks and their temporal evolution. In ICDM, 2021.<\/li>\n\n\n\n - [KDD\u201920] Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin. Structural patterns and generative models of real-world
hypergraphs. In KDD, 2020.<\/li>\n\n\n\n - [ICDM\u201920] Yunbum Kook, Jihoon Ko, and Kijung Shin. Evolution
of real-world hypergraphs: Patterns and models without oracles. In
ICDM, 2020.<\/li>\n\n\n\n - [WWW\u201921] Geon Lee, Minyoung Choe, and Kijung Shin. How do
hyperedges overlap in real-world hypergraphs?-patterns, measures,
and generators. In WWW, 2021.<\/li>\n\n\n\n - [VLDB\u201920] Geon Lee, Jihoon Ko, and Kijung Shin. Hypergraph motifs: concepts, algorithms, and discoveries. PVLDB, 13(12):2256\u20132269,
2020.<\/li>\n\n\n\n - [ICDM\u201921b] Geon Lee and Kijung Shin. THyMe+: Temporal hypergraph motifs and fast algorithms for exact counting. In ICDM,
2021.<\/li>\n\n\n\n - [CommPhys\u201922] Quintino Francesco Lotito, Federico Musciotto,
Alberto Montresor, and Federico Battiston. Higher-order motif
analysis in hypergraphs. Comm. Phys, 5(1):1\u20138, 2022.<\/li>\n\n\n\n - [SIREV\u201921] Leo Torres, Ann S. Blevins, Danielle Bassett, and Tina
Eliassi-Rad. The why, how, and when of representations for complex
systems. SIAM Review 63(3):435-485, 2021
6 RELEVANT TUTORIALS
There have been tutorials on mining of graphs in general, including:<\/li>\n\n\n\n - Graph Structures in Data Mining in KDD 2004
\u2013 http:\/\/www.cs.cmu.edu\/~christos\/TALKS\/KDD04-tut\/
\u2013 This tutorial focuses on (1) topological properties of nodes and
edges, (2) importance measures of nodes, and (3) similarity and
influence between nodes in graphs.<\/li>\n\n\n\n - Large Graph Mining: Patterns, Tools, and Case Studies in CIKM
2008 & ICDE 2009
\u2013 http:\/\/tonghanghang.org\/pdfs\/tut-icde09-part1_patterns.pdf
\u2013 This tutorial focuses on (1) structural patterns, (2) matrix &
tensor tools, (3) proximity measures between nodes, and (4)
case studies of real-world graphs.<\/li>\n\n\n\n - Mining Billion-Scale Graphs: Patterns and Algorithms in SIGMOD
2012
\u2013 https:\/\/www.cs.cmu.edu\/~christos\/TALKS\/12-SIGMOD-tutorial\/
\u2013 This tutorial focuses on (1) patterns in real-world graphs, (2)
tools for pattern mining in graphs, and (3) scalable algorithms
for large-scale graphs.<\/li>\n\n\n\n - Advanced Graph Mining for Community Evaluation in Social Networks and the Web in WSDM 2013
\u2013 http:\/\/www.lix.polytechnique.fr\/~mvazirg\/WSDM2013_tutorial
\u2013 This tutorial focuses on detection and evaluation methods of
communities in graphs.<\/li>\n\n\n\n - Big Graph Mining: Algorithms, Anomaly Detection, and Applications in ASONAM 2013
CIKM \u201922, October 17\u201321, 2022, Atlanta, GA, USA Geon Lee, Jaemin Yoo, & Kijung Shin
\u2013 https:\/\/datalab.snu.ac.kr\/~ukang\/talks\/13-ASONAM-tutorial\/
\u2013 This tutorial focuses on (1) scalable graph mining, (2) graphbased anomaly detection, and (3) applications.<\/li>\n\n\n\n - Core Decomposition of Networks: Concepts, Algorithms and Applications in ICDM 2016 & PKDD 2017
\u2013 https:\/\/fragkiskos.me\/projects\/core_tutorial\/
\u2013 This tutorial focuses on (a) the concept and properties of core
decomposition, (b) efficient computation, and (c) applications.<\/li>\n\n\n\n - Roles in Networks – Foundations, Methods and Applications in
ICDM 2021
\u2013 https:\/\/cswzhang.github.io\/icdm-tutorial-2021\/
\u2013 This tutorial focuses on (a) the taxonomy of role analytic methods, (b) role-based embedding methods, (c) and applications.
To the best of our knowledge, however, no tutorial that focuses on
hypergraphs has been offered in data-mining and related venues.
The patterns, tools and models covered in this tutorial are clearly
distinguished from those for ordinary graphs. We plan to deliver
the same tutorial at DSAA 2022 and ICDM 2022.
7 SHORT BIO OF PRESENTERS
Geon Lee (https:\/\/geonlee0325.github.io) is a Ph.D. student at the
Kim Jaechul Graduate School of AI at KAIST. He received his B.S.
degree in Computer Science and Engineering from Sungkyunkwan
University in 2019. His research interests include graph mining
and its applications. Especially, his studies of hypergraphs have
appeared in major data mining venues, including VLDB, WWW,
and ICDM.
Jaemin Yoo (https:\/\/jaeminyoo.github.io) is a postdoctoral research
fellow in the Heinz College of Information Systems and Public
Policy at Carnegie Mellon University. He received his Ph.D. and
B.S. in Computer Science and Engineering from Seoul National
University. His research interests include probabilistic mining and
machine learning on graphs. His work has been published in major
venues including WWW, KDD, and NeurIPS. He is a recipient of the
Google PhD Fellowship and the Qualcomm Innovation Fellowship.
Kijung Shin (https:\/\/kijungs.github.io\/) is an Ewon Endowed Assistant Professor (jointly affiliated) in the Kim Jaechul Graduate
School of AI and the School of Electrical Engineering at KAIST.
He received his Ph.D. in Computer Science from Carnegie Mellon
University in 2019. He has published more than 50 referred articles
at major data mining venues, and he won the best research paper
award at KDD 2016. His research interests span a wide range of
topics on graph mining, with a focus on scalable algorithm design
and empirical analysis of real-world hypergraphs.
ACKNOWLEDGEMENTS
This work was supported by National Research Foundation of Korea
(NRF) grant funded by the Korea government (MSIT) (No. NRF2020R1C1C1008296) and Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the
Korea government (MSIT) (No. 2022-0-00157, Robust, Fair, Extensible Data-Centric Continual Learning).
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CIKM.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Ben Gilbert 8\/15\/2025 Integration of the RF Hypergraph functionality as a transparent overlay layer on the 3D Cesium Globe. Here’s a summary of what I’ve implemented: RF Hypergraph Transparent Overlay Integration Summary \ud83d\udd27 Integration Components Added: \ud83c\udfaf Key Features: \ud83c\udf10 Integration with Cesium Globe: The hypergraph overlay seamlessly integrates with the existing Cesium visualization by: This creates a… RF Hypergraphs in Satellite Communication Networks<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":426,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-857","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/857","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=857"}],"version-history":[{"count":3,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/857\/revisions"}],"predecessor-version":[{"id":2863,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/857\/revisions\/2863"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/426"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- 5G\/6G networks<\/strong> to manage dynamic spectrum allocation<\/li>\n\n\n\n
- It flags hyperedges<\/strong> with abnormal behavior (e.g., unexpected phase shifts across multiple nodes)<\/li>\n\n\n\n
- The hypergraph model detects nonlinear deviations<\/strong> in signal relationships<\/li>\n\n\n\n
- Hyperedges<\/strong> = Multi-way interactions (e.g., shared spectrum use, beamforming dependencies)<\/li>\n<\/ul>\n\n\n\n
- Nodes<\/strong> = RF components (e.g., antennas, frequency bands, amplifiers)<\/li>\n\n\n\n
- Geolocated Signals<\/strong>: RF signal nodes are positioned based on geographic coordinates<\/li>\n\n\n\n
- Transparent Overlay<\/strong>: The hypergraph visualizer renders transparently over the Cesium 3D globe<\/li>\n\n\n\n
- Start\/Stop<\/strong>\u00a0hypergraph overlay<\/li>\n\n\n\n