{"id":"https://openalex.org/W4416213876","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228884","title":"Biasing second-order random walk sampling for heterogeneous graph embedding <sup>*</sup>","display_name":"Biasing second-order random walk sampling for heterogeneous graph embedding <sup>*</sup>","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416213876","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228884"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228884","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044649325","display_name":"Mauricio Soto","orcid":"https://orcid.org/0000-0001-5977-9467"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Mauricio Soto-Gomez","raw_affiliation_strings":["Universit&#x00E0; Degli Studi di Milano,Dipartimento di Informatica,Milan,Italy"],"affiliations":[{"raw_affiliation_string":"Universit&#x00E0; Degli Studi di Milano,Dipartimento di Informatica,Milan,Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006253386","display_name":"Carlos Cano","orcid":"https://orcid.org/0000-0002-0181-2444"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Carlos Cano","raw_affiliation_strings":["University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain"],"affiliations":[{"raw_affiliation_string":"University of Granada,Department of Computer Science and Artificial Intelligence,Granada,Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073533486","display_name":"Justin Reese","orcid":"https://orcid.org/0000-0002-2170-2250"},"institutions":[{"id":"https://openalex.org/I148283060","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I148283060","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin Reese","raw_affiliation_strings":["Lawrence Berkeley National Laboratory,Berkeley,USA"],"affiliations":[{"raw_affiliation_string":"Lawrence Berkeley National Laboratory,Berkeley,USA","institution_ids":["https://openalex.org/I148283060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033225381","display_name":"Peter N. Robinson","orcid":"https://orcid.org/0000-0002-0736-9199"},"institutions":[{"id":"https://openalex.org/I4210139777","display_name":"Berlin Institute of Health at Charit\u00e9 - Universit\u00e4tsmedizin Berlin","ror":"https://ror.org/0493xsw21","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210139777","https://openalex.org/I7877124"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peter N. Robinson","raw_affiliation_strings":["Universit&#x00E4;tsmedizin,Berlin Institute of Health - Charit&#x00E9;,Berlin,Germany"],"affiliations":[{"raw_affiliation_string":"Universit&#x00E4;tsmedizin,Berlin Institute of Health - Charit&#x00E9;,Berlin,Germany","institution_ids":["https://openalex.org/I4210139777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071444502","display_name":"Giorgio Valentini","orcid":"https://orcid.org/0000-0002-5694-3919"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giorgio Valentini","raw_affiliation_strings":["Universit&#x00E0; Degli Studi di Milano,Dipartimento di Informatica,Milan,Italy"],"affiliations":[{"raw_affiliation_string":"Universit&#x00E0; Degli Studi di Milano,Dipartimento di Informatica,Milan,Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079939753","display_name":"Elena Casiraghi","orcid":"https://orcid.org/0000-0003-2024-7572"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Elena Casiraghi","raw_affiliation_strings":["Universit&#x00E0; Degli Studi di Milano,Dipartimento di Informatica,Milan,Italy"],"affiliations":[{"raw_affiliation_string":"Universit&#x00E0; Degli Studi di Milano,Dipartimento di Informatica,Milan,Italy","institution_ids":["https://openalex.org/I189158943"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5044649325"],"corresponding_institution_ids":["https://openalex.org/I189158943"],"apc_list":null,"apc_paid":null,"fwci":2.8331,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.93076524,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9832000136375427,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9832000136375427,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.00419999985024333,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.0013000000035390258,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6657999753952026},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.6324999928474426},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5597000122070312},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.542900025844574},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.476500004529953},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45350000262260437},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3919999897480011}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6657999753952026},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514999866485596},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5597000122070312},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.542900025844574},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5205000042915344},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.476500004529953},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45350000262260437},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4171000123023987},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3919999897480011},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.3434999883174896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.3109000027179718},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.288100004196167},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228884","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228884","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2145658888","https://openalex.org/W2154851992","https://openalex.org/W2247119764","https://openalex.org/W2283196293","https://openalex.org/W2593560537","https://openalex.org/W2604314403","https://openalex.org/W2728059831","https://openalex.org/W2735272571","https://openalex.org/W2743104969","https://openalex.org/W2767774008","https://openalex.org/W2786016794","https://openalex.org/W2809435521","https://openalex.org/W2891383691","https://openalex.org/W2907492528","https://openalex.org/W2911286998","https://openalex.org/W2962756421","https://openalex.org/W2963844113","https://openalex.org/W2965857891","https://openalex.org/W2982713744","https://openalex.org/W2984834462","https://openalex.org/W3004507689","https://openalex.org/W3012871709","https://openalex.org/W3114303065","https://openalex.org/W4382139355"],"related_works":[],"abstract_inverted_index":{"We":[0,42],"present":[1],"heterogeneous-node2vec,":[2],"a":[3,20,26],"novel":[4],"method":[5],"that":[6,67,78],"leverages":[7],"the":[8,14,30,44,96,100,105,111],"well-known":[9],"node2vec":[10],"algorithm":[11],"to":[12,28,52],"enable":[13],"generation":[15],"of":[16,108],"random-walk":[17,85],"samples":[18],"in":[19],"heterogeneous":[21,63,84,127],"context.":[22],"Specifically,":[23],"we":[24],"propose":[25],"strategy":[27,69],"bias":[29],"random":[31],"walk,":[32],"enabling":[33],"type-aware":[34],"transitions":[35],"between":[36],"different":[37],"node":[38],"and":[39,118,123],"edge":[40],"types.":[41],"evaluate":[43],"proposed":[45],"technique":[46],"on":[47,83,91],"node-label":[48,74],"prediction":[49],"tasks,":[50],"applied":[51],"various":[53],"real-world,":[54],"complex":[55,126],"networks.":[56],"A":[57],"comparison":[58],"with":[59],"state-of-the-art":[60],"techniques":[61],"for":[62,73,121],"graph":[64,79],"embedding":[65],"demonstrates":[66],"our":[68],"achieves":[70],"competitive":[71],"results":[72],"prediction.":[75],"This":[76,113],"evidences":[77],"representation":[80],"methods":[81],"based":[82],"sampling":[86,97],"can":[87],"attain":[88],"strong":[89],"performance":[90],"standard":[92],"supervised":[93],"tasks":[94],"when":[95],"procedure":[98],"incorporates":[99],"semantic":[101],"information":[102],"defined":[103],"by":[104],"type":[106],"heterogeneity":[107],"entities":[109],"within":[110],"graph.":[112],"approach":[114],"provides":[115],"an":[116],"effective":[117],"scalable":[119],"solution":[120],"representing":[122],"learning":[124],"from":[125],"graphs.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
