{"id":"https://openalex.org/W2988874715","doi":"https://doi.org/10.1145/3357384.3358127","title":"Tensor Decomposition-based Node Embedding","display_name":"Tensor Decomposition-based Node Embedding","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2988874715","doi":"https://doi.org/10.1145/3357384.3358127","mag":"2988874715"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3358127","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3358127","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050715780","display_name":"Shah Muhammad Hamdi","orcid":"https://orcid.org/0000-0002-9303-7835"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shah Muhammad Hamdi","raw_affiliation_strings":["Georgia State University, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia State University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010973892","display_name":"Souka\u00efna Filali Boubrahimi","orcid":"https://orcid.org/0000-0001-5693-6383"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soukaina Filali Boubrahimi","raw_affiliation_strings":["Georgia State University, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia State University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009847987","display_name":"Rafal A. Angryk","orcid":"https://orcid.org/0000-0001-9598-8207"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rafal Angryk","raw_affiliation_strings":["Georgia State University, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia State University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.54152943,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2105","last_page":"2108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991000294685364,"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":0.9991000294685364,"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/T12303","display_name":"Tensor decomposition and applications","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9950000047683716,"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/tensor-decomposition","display_name":"Tensor decomposition","score":0.6297892928123474},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6043224930763245},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5883046388626099},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5464531183242798},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5358532667160034},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4390113651752472},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29172003269195557},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.22509407997131348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18076357245445251},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.16736093163490295}],"concepts":[{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.6297892928123474},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6043224930763245},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5883046388626099},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5464531183242798},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5358532667160034},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4390113651752472},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29172003269195557},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.22509407997131348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18076357245445251},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.16736093163490295},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357384.3358127","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3358127","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2024165284","https://openalex.org/W2053186076","https://openalex.org/W2090891622","https://openalex.org/W2116819994","https://openalex.org/W2156718197","https://openalex.org/W2387462954","https://openalex.org/W2469230926","https://openalex.org/W2612872092","https://openalex.org/W2809156873","https://openalex.org/W2950577311","https://openalex.org/W2962756421","https://openalex.org/W3101413764","https://openalex.org/W3104097132"],"related_works":["https://openalex.org/W2393920986","https://openalex.org/W4205920284","https://openalex.org/W3045600977","https://openalex.org/W4287980542","https://openalex.org/W2107886824","https://openalex.org/W3010021662","https://openalex.org/W4289119376","https://openalex.org/W2953288364","https://openalex.org/W3131062520","https://openalex.org/W3062237905"],"abstract_inverted_index":{"In":[0,81],"recent":[1],"years,":[2],"node":[3,60,96],"embedding":[4],"algorithms,":[5],"which":[6],"learn":[7],"low":[8,144],"dimensional":[9,145],"vector":[10,146],"representations":[11,97],"for":[12,94,98,148],"nodes":[13],"in":[14,66,138],"a":[15,52,59,67,91,117,121],"graph,":[16],"have":[17],"been":[18],"one":[19],"of":[20,25,39,46,54,101,116,170],"the":[21,26,40,47,56,64,70,110,125,132,149,168,171,179],"key":[22],"research":[23],"interests":[24],"graph":[27,57,118],"mining":[28],"community.":[29],"The":[30],"existing":[31],"algorithms":[32,76],"either":[33],"rely":[34],"on":[35],"computationally":[36],"expensive":[37],"eigendecomposition":[38],"large":[41],"matrices,":[42],"or":[43],"require":[44],"tuning":[45],"word":[48],"embedding-based":[49],"hyperparameters":[50],"as":[51,58],"result":[53],"representing":[55],"sequence":[61],"similar":[62],"to":[63,79,140,162,167,178],"sentences":[65],"document.":[68],"Moreover,":[69],"latent":[71],"features":[72],"produced":[73],"by":[74,119],"these":[75],"are":[77],"hard":[78],"interpret.":[80],"this":[82],"paper,":[83],"we":[84],"present":[85],"Tensor":[86],"Decomposition-based":[87],"Node":[88],"Embedding":[89],"(TDNE),":[90],"novel":[92],"model":[93,108],"learning":[95],"arbitrary":[99],"types":[100],"graphs:":[102],"undirected,":[103],"directed,":[104],"and/or":[105],"weighted.":[106],"Our":[107,151],"preserves":[109],"local":[111],"and":[112,130,174],"global":[113],"structural":[114],"properties":[115],"constructing":[120],"third-order":[122],"tensor":[123,133],"using":[124,154],"k-step":[126],"transition":[127],"probability":[128],"matrices":[129],"decomposing":[131],"through":[134],"CANDECOMP/PARAFAC":[135],"(CP)":[136],"decomposition":[137],"order":[139],"produce":[141],"an":[142],"interpretable,":[143],"space":[147],"nodes.":[150],"experimental":[152],"evaluation":[153],"two":[155],"well-known":[156],"social":[157],"network":[158,180],"datasets":[159],"proves":[160],"TDNE":[161],"be":[163],"interpretable":[164],"with":[165,176],"respect":[166,177],"understandability":[169],"feature":[172],"space,":[173],"precise":[175],"reconstruction.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}