{"id":"https://openalex.org/W2906828655","doi":"https://doi.org/10.48550/arxiv.1812.10962","title":"A Variational Topological Neural Model for Cascade-based Diffusion in Networks","display_name":"A Variational Topological Neural Model for Cascade-based Diffusion in Networks","publication_year":2018,"publication_date":"2018-12-28","ids":{"openalex":"https://openalex.org/W2906828655","doi":"https://doi.org/10.48550/arxiv.1812.10962","mag":"2906828655"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1812.10962","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.10962","pdf_url":"https://arxiv.org/pdf/1812.10962","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1812.10962","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031635996","display_name":"Sylvain Lamprier","orcid":"https://orcid.org/0000-0002-2508-922X","observed_orcids":["https://orcid.org/0000-0002-2508-922X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lamprier, Sylvain","raw_affiliation_strings":["Machine Learning and Information Access"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning and Information Access","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5031635996"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10320","display_name":"Neural Networks and Applications","score":9.999999747378752e-05,"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/variational-bayesian-inference","display_name":"variational inference","score":0.7139999866485596},{"id":"https://openalex.org/keywords/epidemic-spread-prediction","display_name":"epidemic spread prediction","score":0.36800000071525574}],"concepts":[{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6892513036727905},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6889971494674683},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.6476632952690125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5986114740371704},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5959287285804749},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5622504353523254},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5588762760162354},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.5391924381256104},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5119944214820862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42442643642425537},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.33840203285217285},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23408761620521545},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12355327606201172},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07477101683616638},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C42360764","wikidata":"https://www.wikidata.org/wiki/Q83588","display_name":"Chemical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1812.10962","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.10962","pdf_url":"https://arxiv.org/pdf/1812.10962","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-02074997v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.sorbonne-universite.fr/hal-02074997","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2019","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.1812.10962","doi":"https://doi.org/10.48550/arxiv.1812.10962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1812.10962","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1812.10962","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1812.10962","pdf_url":"https://arxiv.org/pdf/1812.10962","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1495750374","https://openalex.org/W1522301498","https://openalex.org/W2104246439","https://openalex.org/W2127492100","https://openalex.org/W2134747589","https://openalex.org/W2296752489","https://openalex.org/W2509830164","https://openalex.org/W2740189214","https://openalex.org/W2798918712","https://openalex.org/W2951004968"],"related_works":["https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W1566155057","https://openalex.org/W2060986072","https://openalex.org/W2052574922","https://openalex.org/W64588465","https://openalex.org/W3120641340","https://openalex.org/W2117825986","https://openalex.org/W3134067061","https://openalex.org/W2079855347"],"abstract_inverted_index":{"Many":[0],"works":[1],"have":[2],"been":[3],"proposed":[4],"in":[5,14,31,65,98],"the":[6,10,53,61,74,78,85,99],"literature":[7],"to":[8,24],"capture":[9],"dynamics":[11],"of":[12,18,39,55,63,87],"diffusion":[13,35,64,119],"networks.":[15],"While":[16],"some":[17],"them":[19],"define":[20],"graphical":[21],"markovian":[22],"models":[23],"extract":[25],"temporal":[26],"relationships":[27],"between":[28],"node":[29],"infections":[30,40],"networks,":[32],"others":[33],"consider":[34],"episodes":[36],"as":[37,68],"sequences":[38],"via":[41],"recurrent":[42,110],"neural":[43,111],"models.":[44],"In":[45],"this":[46],"paper":[47],"we":[48],"propose":[49,107],"a":[50,82,108],"model":[51,112],"at":[52],"crossroads":[54],"these":[56],"two":[57],"extremes,":[58],"which":[59,101,113],"embeds":[60],"history":[62],"infected":[66],"nodes":[67],"hidden":[69,97],"continuous":[70],"states.":[71],"Depending":[72],"on":[73],"trajectory":[75],"followed":[76],"by":[77],"content":[79,93],"before":[80],"reaching":[81],"given":[83],"node,":[84],"distribution":[86],"influence":[88],"probabilities":[89],"may":[90],"vary.":[91],"However,":[92],"trajectories":[94],"are":[95],"usually":[96],"data,":[100],"induces":[102],"challenging":[103],"learning":[104],"problems.":[105],"We":[106],"topological":[109],"exhibits":[114],"good":[115],"experimental":[116],"performances":[117],"for":[118],"modelling":[120],"and":[121],"prediction.":[122]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
