{"id":"https://openalex.org/W7161288714","doi":"https://doi.org/10.48550/arxiv.2605.13863","title":"Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks","display_name":"Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7161288714","doi":"https://doi.org/10.48550/arxiv.2605.13863"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.13863","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070245509","display_name":"Abdul Joseph Fofanah","orcid":"https://orcid.org/0000-0001-8742-9325"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fofanah, Abdul Joseph","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136205034","display_name":"Lian Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Lian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136211555","display_name":"David Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099685026","display_name":"Tsungcheng Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Tsungcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136257713","display_name":"Kwabena Sarpong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarpong, Kwabena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.6775000095367432,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.6775000095367432,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.1876000016927719,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.06129999831318855,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7271999716758728},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6972000002861023},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5928000211715698},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5735999941825867},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5717999935150146},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49059998989105225},{"id":"https://openalex.org/keywords/spike-timing-dependent-plasticity","display_name":"Spike-timing-dependent plasticity","score":0.4693000018596649},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.4207000136375427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7434999942779541},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7271999716758728},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6972000002861023},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5928000211715698},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5717999935150146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.551800012588501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49059998989105225},{"id":"https://openalex.org/C159919123","wikidata":"https://www.wikidata.org/wiki/Q7577157","display_name":"Spike-timing-dependent plasticity","level":4,"score":0.4693000018596649},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3506999909877777},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.2786000072956085},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2718000113964081},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2703999876976013}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.13863","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13863","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9043022394180298,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Anomaly":[0,40],"detection":[1,54,69,190],"in":[2,19,55,135],"dynamic":[3,56,183],"networks":[4,47],"is":[5],"critical":[6],"for":[7,38,51,199],"applications":[8],"from":[9],"cybersecurity":[10],"to":[11,150,172],"industrial":[12],"monitoring,":[13],"yet":[14],"existing":[15],"methods":[16],"face":[17],"challenges":[18],"energy":[20,197],"efficiency,":[21],"temporal":[22,75,111,115],"precision,":[23],"and":[24,66,113,138,164,184,196],"adaptability.":[25],"This":[26],"paper":[27],"introduces":[28],"ASTDP-GAD,":[29],"a":[30],"novel":[31],"Adaptive":[32],"Spiking":[33],"Temporal":[34],"Dynamics":[35],"Plasticity":[36],"framework":[37,59],"Graph":[39],"Detection":[41],"that":[42],"integrates":[43],"spiking":[44,61,104],"graph":[45,77,86],"neural":[46,62],"with":[48,79,88,94,117,131,170],"STDP":[49,64,107,160],"learning":[50,161],"energy-efficient":[52],"neuromorphic":[53,200],"networks.":[57],"Our":[58],"unifies":[60],"computation,":[63],"learning,":[65],"graph-based":[67],"anomaly":[68,119,157,189],"through":[70],"the":[71],"following":[72],"key":[73],"innovations:":[74],"spike":[76,98,126],"encoding":[78,127],"adaptive":[80,106],"Leaky":[81],"Integrate-and-Fire":[82],"(LIF)":[83],"dynamics;":[84],"LIF-based":[85],"attention":[87,145],"lateral":[89],"inhibition;":[90],"event-driven":[91],"hypergraph":[92,147],"memory":[93,148],"STDP-inspired":[95],"prototype":[96],"updates;":[97],"rate":[99],"contrast":[100,153],"pooling":[101,154],"based":[102],"on":[103,178,181],"irregularity;":[105],"layers":[108],"capturing":[109],"causal":[110],"relationships;":[112],"multi-scale":[114],"convolution":[116],"multi-factor":[118,165],"fusion.":[120],"Theoretical":[121],"analysis":[122],"provides":[123],"rigorous":[124],"guarantees:":[125],"preserves":[128],"input":[129],"information":[130],"resolution":[132],"scaling":[133],"linearly":[134],"simulation":[136],"steps":[137],"hidden":[139],"dimension;":[140],"LIFGAT":[141],"approximates":[142],"any":[143],"continuous":[144],"function;":[146],"converges":[149,162],"optimal":[151],"prototypes;":[152],"achieves":[155],"provable":[156],"selection":[158],"bounds;":[159],"stably;":[163],"fusion":[166],"produces":[167],"calibrated":[168],"scores":[169],"up":[171],"$5\\times$":[173],"variance":[174],"reduction.":[175],"Extensive":[176],"experiments":[177],"nine":[179],"datasets":[180],"both":[182],"static":[185],"graphs":[186],"demonstrate":[187],"superior":[188],"accuracy":[191],"while":[192],"maintaining":[193],"biological":[194],"plausibility":[195],"efficiency":[198],"deployment.":[201]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-16T00:00:00"}
