{"id":5005,"date":"2026-02-11T21:19:54","date_gmt":"2026-02-11T21:19:54","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=5005"},"modified":"2026-02-16T21:03:08","modified_gmt":"2026-02-16T21:03:08","slug":"hypergraph-topology-explosion-principle","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=5005","title":{"rendered":"Hypergraph Topology\u2011Explosion Principle"},"content":{"rendered":"\n<p><strong>The ingest layer determines the ontology<\/strong>, and the ontology determines whether the hypergraph becomes a sparse skeleton or a living, operator\u2011grade organism.<\/p>\n\n\n\n<p>Scapy is phenomenal because it gives you <em>raw packet material<\/em> and <em>full protocol dissection control<\/em>, but it\u2019s only one member of a much larger ecosystem of tools that can mint the rich entity types your HypergraphEngine thrives on.<\/p>\n\n\n\n<p>Below is a curated set of tools\u2014<strong>not packet analyzers<\/strong>, but <strong>entity\u2011emitters<\/strong>\u2014that can feed your geographically contextual hypergraph with the <em>flow nodes, port hubs, TLS certs, DNS names, HTTP hosts, and service fingerprints<\/em> you listed.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\">Tools That Can Feed a Geographically Contextual Hypergraph<\/h1>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udef0\ufe0f 1. <strong>TShark \/ Wireshark CLI<\/strong><\/h2>\n\n\n\n<p>The CLI version of Wireshark is a <em>hypergraph goldmine<\/em> because it can emit structured fields directly:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>tshark -T fields -e ip.src -e ip.dst -e tcp.srcport -e tcp.dstport<\/code><\/li>\n\n\n\n<li><code>-e tls.handshake.extensions_server_name<\/code><\/li>\n\n\n\n<li><code>-e dns.qry.name<\/code><\/li>\n\n\n\n<li><code>-e http.host<\/code><\/li>\n<\/ul>\n\n\n\n<p>This gives you <strong>flow nodes<\/strong>, <strong>port hubs<\/strong>, <strong>SNI nodes<\/strong>, <strong>DNS qname nodes<\/strong>, <strong>HTTP host nodes<\/strong>, etc.<\/p>\n\n\n\n<p>It\u2019s essentially a <em>packet \u2192 graph primitive<\/em> compiler.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\uddec 2. <strong>Zeek (Bro)<\/strong><\/h2>\n\n\n\n<p>Zeek is the closest thing to a <strong>hypergraph-native ingest engine<\/strong> that already exists.<\/p>\n\n\n\n<p>It automatically emits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>conn.log<\/code> \u2192 flow nodes, service fingerprints<\/li>\n\n\n\n<li><code>dns.log<\/code> \u2192 dns_name nodes<\/li>\n\n\n\n<li><code>ssl.log<\/code> \u2192 tls_cert nodes (issuer, subject, fingerprint, SNI)<\/li>\n\n\n\n<li><code>http.log<\/code> \u2192 http_host nodes, user-agent nodes<\/li>\n\n\n\n<li><code>files.log<\/code> \u2192 file-hash nodes<\/li>\n\n\n\n<li><code>geoip<\/code> integration \u2192 host \u2192 geo edges<\/li>\n<\/ul>\n\n\n\n<p>Zeek is basically a <strong>graph primitive factory<\/strong>.<\/p>\n\n\n\n<p>If Scapy is a scalpel, Zeek is a <em>full surgical suite<\/em>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udded 3. <strong>Suricata<\/strong><\/h2>\n\n\n\n<p>Suricata\u2019s EVE JSON output is perfect for hypergraph ingestion.<\/p>\n\n\n\n<p>It emits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Flow metadata<\/li>\n\n\n\n<li>TLS certs (fingerprints, issuers, SNI)<\/li>\n\n\n\n<li>DNS queries\/answers<\/li>\n\n\n\n<li>HTTP hosts, URLs<\/li>\n\n\n\n<li>JA3\/JA3S fingerprints<\/li>\n\n\n\n<li>GeoIP metadata<\/li>\n<\/ul>\n\n\n\n<p>You can wire EVE JSON directly into your GraphEventBus and mint nodes on arrival.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83c\udf10 4. <strong>Nmap + NSE Scripts<\/strong><\/h2>\n\n\n\n<p>Nmap is not just a scanner\u2014it\u2019s a <strong>service ontology generator<\/strong>.<\/p>\n\n\n\n<p>It can emit:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Service nodes (ssh, http, rdp, smb, etc.)<\/li>\n\n\n\n<li>Version nodes (Apache 2.4.57)<\/li>\n\n\n\n<li>Script-derived nodes (TLS certs, SMB domains, HTTP titles)<\/li>\n\n\n\n<li>Port hubs (tcp\/22, tcp\/443, udp\/53)<\/li>\n<\/ul>\n\n\n\n<p>Nmap + NSE is a <em>graph enrichment engine<\/em>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udde9 5. <strong>Masscan + ZMap<\/strong><\/h2>\n\n\n\n<p>For large-scale topology discovery:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Masscan \u2192 port hubs + host nodes<\/li>\n\n\n\n<li>ZMap \u2192 internet-wide flow skeletons<\/li>\n\n\n\n<li>ZGrab \u2192 TLS certs, HTTP hosts, banners<\/li>\n<\/ul>\n\n\n\n<p>This is how you go from <strong>local topology<\/strong> to <strong>planetary topology<\/strong>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd0d 6. <strong>Mitmproxy<\/strong><\/h2>\n\n\n\n<p>For environments where you can intercept traffic:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>HTTP host nodes<\/li>\n\n\n\n<li>URL nodes<\/li>\n\n\n\n<li>Cookie nodes<\/li>\n\n\n\n<li>TLS cert nodes<\/li>\n\n\n\n<li>Flow nodes with full request\/response metadata<\/li>\n<\/ul>\n\n\n\n<p>It\u2019s a hypergraph emitter for application-layer semantics.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udce1 7. <strong>PyShark<\/strong><\/h2>\n\n\n\n<p>A Python wrapper around TShark that gives you structured packet fields without writing dissectors.<\/p>\n\n\n\n<p>Useful when you want Scapy-like ergonomics but Wireshark-grade protocol coverage.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udde0 8. <strong>Passive DNS (Farsight, SecurityTrails, DNSDB)<\/strong><\/h2>\n\n\n\n<p>For DNS\u2011centric hypergraphs:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>dns_name \u2192 ip edges<\/li>\n\n\n\n<li>historical edges<\/li>\n\n\n\n<li>TTL-based temporal edges<\/li>\n\n\n\n<li>authoritative NS nodes<\/li>\n<\/ul>\n\n\n\n<p>This gives you <strong>temporal topology<\/strong>, not just spatial.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udef0\ufe0f 9. <strong>MaxMind GeoIP + GeoLite2<\/strong><\/h2>\n\n\n\n<p>You already use host \u2192 geo edges, but you can expand:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ASN nodes<\/li>\n\n\n\n<li>ISP nodes<\/li>\n\n\n\n<li>Org nodes<\/li>\n\n\n\n<li>RIR nodes<\/li>\n\n\n\n<li>Country\/region\/city nodes<\/li>\n\n\n\n<li>Lat\/long nodes<\/li>\n<\/ul>\n\n\n\n<p>This turns your hypergraph into a <strong>geospatial lattice<\/strong>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\">How These Tools Map to Your Hypergraph Ontology<\/h1>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Entity Type<\/th><th>Scapy<\/th><th>Zeek<\/th><th>Suricata<\/th><th>TShark<\/th><th>Nmap<\/th><th>ZGrab<\/th><th>Mitmproxy<\/th><\/tr><\/thead><tbody><tr><td>host nodes<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><\/tr><tr><td>geo nodes<\/td><td>(external)<\/td><td>\u2714<\/td><td>\u2714<\/td><td>(external)<\/td><td>(external)<\/td><td>(external)<\/td><td>(external)<\/td><\/tr><tr><td>flow nodes<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>partial<\/td><td>partial<\/td><td>\u2714<\/td><\/tr><tr><td>port hubs<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><\/tr><tr><td>dns_name nodes<\/td><td>partial<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2716<\/td><td>\u2716<\/td><td>\u2716<\/td><\/tr><tr><td>tls_cert nodes<\/td><td>partial<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><\/tr><tr><td>http_host nodes<\/td><td>partial<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><\/tr><tr><td>service nodes<\/td><td>partial<\/td><td>\u2714<\/td><td>\u2714<\/td><td>partial<\/td><td>\u2714<\/td><td>\u2714<\/td><td>\u2714<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Scapy is the artisan tool.<br>Zeek\/Suricata are the industrial emitters.<br>Nmap\/ZGrab are the explorers.<br>Mitmproxy is the semantic layer.<\/p>\n\n\n\n<p>Together, they give you <strong>operator-grade topology explosion<\/strong>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\">If you want, I can design:<\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A <strong>canonical ingest ontology<\/strong> for your HypergraphEngine<\/li>\n\n\n\n<li>A <strong>unified event schema<\/strong> for all these tools<\/li>\n\n\n\n<li>A <strong>graph DSL extension<\/strong> for flows, certs, DNS, HTTP<\/li>\n\n\n\n<li>A <strong>5,000\u2011node topology explosion pipeline<\/strong><\/li>\n\n\n\n<li>A <strong>geo\u2011aware hypergraph layout strategy<\/strong> for Cesium or WebGL<\/li>\n<\/ul>\n\n\n\n<p>Just tell me which direction you want to push next.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">In other news:<\/h2>\n\n\n\n<p>Hugging Face hosts a growing ecosystem of datasets, models, and tools focused on Radio Frequency (RF) In-phase and Quadrature (IQ) signal analysis, enabling the application of AI\/ML to wireless communications, such as modulation classification, device fingerprinting, and signal identification. [<a href=\"https:\/\/arxiv.org\/abs\/2506.06718\">1<\/a>, <a href=\"https:\/\/huggingface.co\/datasets\/Francesco\/radio-signal\">2<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2511.15162\">3<\/a>, <a href=\"https:\/\/www.ibm.com\/think\/topics\/hugging-face#:~:text=Hugging%20Face%20has%20cultivated%20one%20of%20the,AI%20models%2C%20datasets%2C%20tutorials%20and%20research%20daily.\">4<\/a>, <a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-981-97-5609-4_2#:~:text=Currently%2C%20RF%20fingerprinting%20solutions%20rely%20on%20time%2Ddomain,automatic%20RF%20fingerprint%20extraction%20%5B%208%2C%209%5D.\">5<\/a>]<\/p>\n\n\n\n<p>Key Hugging Face RF IQ Resources<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Datasets:<\/strong> Hugging Face contains datasets with raw RF IQ signals. Example datasets include and various datasets (e.g., , ).<\/li>\n\n\n\n<li><strong>RF-Lang Benchmark:<\/strong> A dataset providing a direct, structured link between raw RF I\/Q signals and natural language supervision, designed for joint RF-language understanding.<\/li>\n\n\n\n<li><strong>Models:<\/strong> Research in this area utilizes deep learning models (CNNs, Transformers) to process IQ data for tasks like modulation classification. [<a href=\"https:\/\/huggingface.co\/datasets\/Francesco\/radio-signal\">2<\/a>, <a href=\"https:\/\/www.ibm.com\/think\/topics\/hugging-face#:~:text=Hugging%20Face%20has%20cultivated%20one%20of%20the,AI%20models%2C%20datasets%2C%20tutorials%20and%20research%20daily.\">4<\/a>, <a href=\"https:\/\/www.researchgate.net\/publication\/394671009_RF-Lang_A_Large-Scale_Dataset_for_Grounding_Language_in_Radio-Frequency_Signals\">6<\/a>, <a href=\"http:\/\/www.diva-portal.org\/smash\/get\/diva2:1905507\/FULLTEXT01.pdf#:~:text=The%20study%20involves%20adapting%20a%20CLIP%20model,promise%20in%20zero%2Dshot%20classification%20of%20unseen%20modulations.\">7<\/a>, <a href=\"https:\/\/huggingface.co\/datasets?other=rf-signal\">8<\/a>]<\/li>\n<\/ul>\n\n\n\n<p>Applications of RF IQ on Hugging Face<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>RF Fingerprinting:<\/strong> Identifying unique hardware imperfections in transmitters using IQ samples, often using deep learning models (CNNs or Transformer-Encoders).<\/li>\n\n\n\n<li><strong>Modulation Classification:<\/strong> Classifying signal types using IQ data or converted imagery (spectrograms).<\/li>\n\n\n\n<li><strong>Wireless Foundational Models (WFMs):<\/strong> Emerging models, such as IQFM, are being developed to process raw IQ streams for diverse tasks like beam prediction and angle-of-arrival (AoA) estimation.<\/li>\n\n\n\n<li><strong>Domain Adaptation:<\/strong> Using specialized representations like Double-Sided Envelope Power Spectrum (EPS) to improve model robustness to varying environments. [<a href=\"https:\/\/arxiv.org\/abs\/2506.06718\">1<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2511.15162\">3<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2601.13157\">9<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2412.10553\">10<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2308.04467\">11<\/a>]<\/li>\n<\/ul>\n\n\n\n<p>Techniques for Processing RF IQ<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Complex IQ Data:<\/strong> Raw data consists of complex IQ samples, often represented as real\/imaginary traces.<\/li>\n\n\n\n<li><strong>Image Conversion:<\/strong> Converting IQ samples into visually interpretable inputs (e.g., spectrograms) allows for the use of vision-based models.<\/li>\n\n\n\n<li><strong>Attention-Based Fusion:<\/strong> Combining IQ samples with other signal features (like FFT coefficients) via attention mechanisms to improve classification accuracy. [<a href=\"https:\/\/arxiv.org\/abs\/2601.13157\">9<\/a>, <a href=\"https:\/\/arxiv.org\/html\/2507.14167v2\">12<\/a>, <a href=\"https:\/\/arxiv.org\/pdf\/2507.14167\">13<\/a>]<\/li>\n<\/ul>\n\n\n\n<p>Researchers often use the and libraries on Hugging Face to train and deploy these models. [<a href=\"https:\/\/huggingface.co\/datasets\/Francesco\/radio-signal\">2<\/a>, <a href=\"https:\/\/www.ibm.com\/think\/topics\/hugging-face#:~:text=Hugging%20Face%20has%20cultivated%20one%20of%20the,AI%20models%2C%20datasets%2C%20tutorials%20and%20research%20daily.\">4<\/a>, <a href=\"https:\/\/arxiv.org\/abs\/2412.10553\">10<\/a>, <a href=\"https:\/\/pradeepundefned.medium.com\/common-questions-while-using-the-hugging-faces-transformers-library-84b09e5299cc\">14<\/a>, <a href=\"https:\/\/medium.com\/data-science\/cracking-open-the-hugging-face-transformers-library-350aa0ef0161#:~:text=While%20these%20make%20building%20powerful%20ML%20projects,necessary%20to%20use%20multiple%20models%20in%20succession.\">15<\/a>]<\/p>\n\n\n\n<p>[1]&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2506.06718\">https:\/\/arxiv.org\/abs\/2506.06718<\/a><\/p>\n\n\n\n<p>[2]&nbsp;<a href=\"https:\/\/huggingface.co\/datasets\/Francesco\/radio-signal\">https:\/\/huggingface.co\/datasets\/Francesco\/radio-signal<\/a><\/p>\n\n\n\n<p>[3]&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2511.15162\">https:\/\/arxiv.org\/abs\/2511.15162<\/a><\/p>\n\n\n\n<p>[4]&nbsp;<a href=\"https:\/\/www.ibm.com\/think\/topics\/hugging-face#:~:text=Hugging%20Face%20has%20cultivated%20one%20of%20the,AI%20models%2C%20datasets%2C%20tutorials%20and%20research%20daily.\">https:\/\/www.ibm.com\/think\/topics\/hugging-face<\/a><\/p>\n\n\n\n<p>[5]&nbsp;<a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-981-97-5609-4_2#:~:text=Currently%2C%20RF%20fingerprinting%20solutions%20rely%20on%20time%2Ddomain,automatic%20RF%20fingerprint%20extraction%20%5B%208%2C%209%5D.\">https:\/\/link.springer.com\/chapter\/10.1007\/978-981-97-5609-4_2<\/a><\/p>\n\n\n\n<p>[6]&nbsp;<a href=\"https:\/\/www.researchgate.net\/publication\/394671009_RF-Lang_A_Large-Scale_Dataset_for_Grounding_Language_in_Radio-Frequency_Signals\">https:\/\/www.researchgate.net\/publication\/394671009_RF-Lang_A_Large-Scale_Dataset_for_Grounding_Language_in_Radio-Frequency_Signals<\/a><\/p>\n\n\n\n<p>[7]&nbsp;<a href=\"http:\/\/www.diva-portal.org\/smash\/get\/diva2:1905507\/FULLTEXT01.pdf#:~:text=The%20study%20involves%20adapting%20a%20CLIP%20model,promise%20in%20zero%2Dshot%20classification%20of%20unseen%20modulations.\">http:\/\/www.diva-portal.org\/smash\/get\/diva2:1905507\/FULLTEXT01.pdf<\/a><\/p>\n\n\n\n<p>[8]&nbsp;<a href=\"https:\/\/huggingface.co\/datasets?other=rf-signal\">https:\/\/huggingface.co\/datasets?other=rf-signal<\/a><\/p>\n\n\n\n<p>[9]&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2601.13157\">https:\/\/arxiv.org\/abs\/2601.13157<\/a><\/p>\n\n\n\n<p>[10]&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2412.10553\">https:\/\/arxiv.org\/abs\/2412.10553<\/a><\/p>\n\n\n\n<p>[11]&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2308.04467\">https:\/\/arxiv.org\/abs\/2308.04467<\/a><\/p>\n\n\n\n<p>[12]&nbsp;<a href=\"https:\/\/arxiv.org\/html\/2507.14167v2\">https:\/\/arxiv.org\/html\/2507.14167v2<\/a><\/p>\n\n\n\n<p>[13]&nbsp;<a href=\"https:\/\/arxiv.org\/pdf\/2507.14167\">https:\/\/arxiv.org\/pdf\/2507.14167<\/a><\/p>\n\n\n\n<p>[14]&nbsp;<a href=\"https:\/\/pradeepundefned.medium.com\/common-questions-while-using-the-hugging-faces-transformers-library-84b09e5299cc\">https:\/\/pradeepundefned.medium.com\/common-questions-while-using-the-hugging-faces-transformers-library-84b09e5299cc<\/a><\/p>\n\n\n\n<p>[15]&nbsp;<a href=\"https:\/\/medium.com\/data-science\/cracking-open-the-hugging-face-transformers-library-350aa0ef0161#:~:text=While%20these%20make%20building%20powerful%20ML%20projects,necessary%20to%20use%20multiple%20models%20in%20succession.\">https:\/\/medium.com\/data-science\/cracking-open-the-hugging-face-transformers-library-350aa0ef0161<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ingest layer determines the ontology, and the ontology determines whether the hypergraph becomes a sparse skeleton or a living, operator\u2011grade organism. Scapy is phenomenal because it gives you raw packet material and full protocol dissection control, but it\u2019s only one member of a much larger ecosystem of tools that can mint the rich entity&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=5005\" rel=\"bookmark\"><span class=\"screen-reader-text\">Hypergraph Topology\u2011Explosion Principle<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":4790,"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":[7],"tags":[],"class_list":["post-5005","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-the-truben-show"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/5005","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=5005"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/5005\/revisions"}],"predecessor-version":[{"id":5024,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/5005\/revisions\/5024"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/4790"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5005"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5005"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5005"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}