{"id":"https://openalex.org/W2042277073","doi":"https://doi.org/10.1145/2661829.2662056","title":"Learning Interactions for Social Prediction in Large-scale Networks","display_name":"Learning Interactions for Social Prediction in Large-scale Networks","publication_year":2014,"publication_date":"2014-11-03","ids":{"openalex":"https://openalex.org/W2042277073","doi":"https://doi.org/10.1145/2661829.2662056","mag":"2042277073"},"language":"en","primary_location":{"id":"doi:10.1145/2661829.2662056","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2661829.2662056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on 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/A5101593848","display_name":"Xiaofeng Yu","orcid":"https://orcid.org/0000-0002-6770-7532"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaofeng Yu","raw_affiliation_strings":["HP Labs China, Beijing, China","HP Labs China Beijing, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Labs China, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"HP Labs China Beijing, China#TAB#","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005723350","display_name":"Junqing Xie","orcid":"https://orcid.org/0000-0002-0040-0042"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junqing Xie","raw_affiliation_strings":["HP Labs China, Beijing, China","HP Labs China Beijing, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Labs China, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"HP Labs China Beijing, China#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"161","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/homophily","display_name":"Homophily","score":0.9145843386650085},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7734482288360596},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6979795694351196},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.6154077649116516},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5804140567779541},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5234935879707336},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4921197295188904},{"id":"https://openalex.org/keywords/interpersonal-ties","display_name":"Interpersonal ties","score":0.47229817509651184},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38058045506477356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36329254508018494},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3551705479621887},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.20013904571533203},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.10326564311981201}],"concepts":[{"id":"https://openalex.org/C2779812341","wikidata":"https://www.wikidata.org/wiki/Q5891525","display_name":"Homophily","level":2,"score":0.9145843386650085},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734482288360596},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6979795694351196},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.6154077649116516},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5804140567779541},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5234935879707336},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4921197295188904},{"id":"https://openalex.org/C142189719","wikidata":"https://www.wikidata.org/wiki/Q1276633","display_name":"Interpersonal ties","level":2,"score":0.47229817509651184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38058045506477356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36329254508018494},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3551705479621887},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.20013904571533203},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.10326564311981201},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2661829.2662056","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2661829.2662056","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W24664060","https://openalex.org/W950821216","https://openalex.org/W1514107797","https://openalex.org/W1516111018","https://openalex.org/W1584407334","https://openalex.org/W1993995511","https://openalex.org/W1996816151","https://openalex.org/W2008791124","https://openalex.org/W2020488968","https://openalex.org/W2022867359","https://openalex.org/W2039937371","https://openalex.org/W2061901927","https://openalex.org/W2063774819","https://openalex.org/W2064134731","https://openalex.org/W2076844992","https://openalex.org/W2093219534","https://openalex.org/W2101196063","https://openalex.org/W2107933610","https://openalex.org/W2109469951","https://openalex.org/W2120340025","https://openalex.org/W2130354913","https://openalex.org/W2133439966","https://openalex.org/W2135838082","https://openalex.org/W2138782491","https://openalex.org/W2144858342","https://openalex.org/W2152463966","https://openalex.org/W2156273451","https://openalex.org/W2167699886","https://openalex.org/W2170772554","https://openalex.org/W2292926678","https://openalex.org/W2321650456","https://openalex.org/W2949567784","https://openalex.org/W2996781425","https://openalex.org/W3016641327","https://openalex.org/W4234057457","https://openalex.org/W4244387885","https://openalex.org/W4293052541","https://openalex.org/W6630727257","https://openalex.org/W6634886684","https://openalex.org/W6811933453","https://openalex.org/W7029622440"],"related_works":["https://openalex.org/W1698285018","https://openalex.org/W2766104543","https://openalex.org/W1971924293","https://openalex.org/W2990959093","https://openalex.org/W3175275009","https://openalex.org/W2121856305","https://openalex.org/W3150405526","https://openalex.org/W4390684549","https://openalex.org/W3129454375","https://openalex.org/W390164533"],"abstract_inverted_index":{"Social":[0],"networks":[1],"have":[2,11],"already":[3],"emerged":[4],"as":[5],"inconceivably":[6],"vast":[7],"information":[8,19],"repositories":[9],"and":[10,18,36,40,53,61,82,95,116,123,174],"provided":[12],"great":[13],"opportunities":[14],"for":[15,34,84],"social":[16,27,38,42,47,56,86,97,108,159,167],"connection":[17],"diffusion.":[20],"In":[21],"light":[22],"of":[23,55,176],"these":[24],"notable":[25],"outcomes,":[26],"prediction":[28],"is":[29],"a":[30,68],"critical":[31],"research":[32],"goal":[33],"analyzing":[35],"understanding":[37],"media":[39],"online":[41],"networks.":[43,109,160],"We":[44,66,133,161],"investigate":[45],"underlying":[46],"theories":[48],"that":[49],"drive":[50],"the":[51,62,141,152,172],"characteristics":[52],"dynamics":[54],"networks,":[57],"including":[58],"homophily,":[59],"heterophily,":[60],"structural":[63],"hole":[64],"theories.":[65],"propose":[67,134],"unified":[69],"coherent":[70],"framework,":[71],"namely":[72],"mutual":[73,80],"latent":[74],"random":[75],"graphs":[76],"(MLRGs),":[77],"to":[78,126,139,156,170],"exploit":[79],"interactions":[81],"benefits":[83],"predicting":[85],"actions":[87,122],"(e.g.,":[88,99],"users'":[89,121,124],"behaviors,":[90],"opinions,":[91],"preferences":[92],"or":[93,113],"interests)":[94],"discovering":[96],"ties":[98,125],"multiple":[100],"labeled":[101],"relationships":[102],"between":[103],"users)":[104],"simultaneously":[105],"in":[106],"large-scale":[107,158],"MLRGs":[110],"introduce":[111],"latent,":[112],"hidden":[114],"factors":[115],"coupled":[117],"models":[118],"with":[119],"users,":[120],"flexibly":[127],"encode":[128],"evidences":[129],"from":[130],"both":[131],"sources.":[132],"an":[135],"approximate":[136],"optimization":[137],"algorithm":[138,149],"learn":[140],"model":[142],"parameters":[143],"efficiently.":[144],"Furthermore,":[145],"we":[146],"speedup":[147],"this":[148],"based":[150],"on":[151,164],"Hadoop":[153],"MapReduce":[154],"framework":[155],"handle":[157],"performed":[162],"experiments":[163],"two":[165],"real-world":[166],"networking":[168],"datasets":[169],"demonstrate":[171],"validity":[173],"competitiveness":[175],"our":[177],"approach.":[178]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}