{"id":"https://openalex.org/W4368755504","doi":"https://doi.org/10.48550/arxiv.2305.02190","title":"Rethinking Graph Lottery Tickets: Graph Sparsity Matters","display_name":"Rethinking Graph Lottery Tickets: Graph Sparsity Matters","publication_year":2023,"publication_date":"2023-05-03","ids":{"openalex":"https://openalex.org/W4368755504","doi":"https://doi.org/10.48550/arxiv.2305.02190"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.02190","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02190","pdf_url":"https://arxiv.org/pdf/2305.02190","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.02190","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008721965","display_name":"Bo Hui","orcid":"https://orcid.org/0000-0002-2651-1963"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hui, Bo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044218586","display_name":"Da Yan","orcid":"https://orcid.org/0000-0003-2399-723X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Da","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074448953","display_name":"Xiaolong Ma","orcid":"https://orcid.org/0000-0003-1392-2787"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Xiaolong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001457193","display_name":"Wei\u2010Shinn Ku","orcid":"https://orcid.org/0000-0001-8636-4689"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ku, Wei-Shinn","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008721965"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9549000263214111,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9223999977111816,"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/adjacency-matrix","display_name":"Adjacency matrix","score":0.7496141791343689},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6824536323547363},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5534774661064148},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.46747303009033203},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4300573468208313},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4079650640487671},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34227585792541504}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.7496141791343689},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6824536323547363},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5534774661064148},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.46747303009033203},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4300573468208313},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4079650640487671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34227585792541504}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.02190","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02190","pdf_url":"https://arxiv.org/pdf/2305.02190","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.02190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.02190","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.02190","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.02190","pdf_url":"https://arxiv.org/pdf/2305.02190","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4368755504.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2117632582","https://openalex.org/W4388347373"],"abstract_inverted_index":{"Lottery":[0],"Ticket":[1],"Hypothesis":[2],"(LTH)":[3],"claims":[4],"the":[5,27,52,57,61,67,70,75,95,104,122,130,146,161,166,182,192,198,206,209,226,239],"existence":[6],"of":[7,69,97,145,194,213,241],"a":[8,12,98,109,134,153,186,214],"winning":[9,211],"ticket":[10,212,230],"(i.e.,":[11],"properly":[13,141],"pruned":[14],"sub-network":[15],"together":[16],"with":[17],"original":[18,28],"weight":[19,76],"initialization)":[20],"that":[21,83,94],"can":[22],"achieve":[23],"competitive":[24],"performance":[25,89,96,120],"to":[26,38,87,117,158,190],"dense":[29],"network.":[30],"A":[31],"recent":[32],"work,":[33],"called":[34],"UGS,":[35,247],"extended":[36],"LTH":[37],"prune":[39],"graph":[40,53,71,105,123,174,199,220,228],"neural":[41],"networks":[42],"(GNNs)":[43],"for":[44,219],"effectively":[45],"accelerating":[46],"GNN":[47,100,119,215],"inference.":[48],"UGS":[49,128],"simultaneously":[50],"prunes":[51,129],"adjacency":[54,72,131,147,168],"matrix":[55,73,132],"and":[56,74,248],"model":[58],"weights":[59],"using":[60,133],"same":[62],"masking":[63],"mechanism,":[64],"but":[65],"since":[66],"roles":[68],"matrices":[77],"are":[78],"very":[79],"different,":[80],"we":[81,92,113,151,180],"find":[82,93],"their":[84],"sparsifications":[85],"lead":[86],"different":[88],"characteristics.":[90],"Specifically,":[91],"sparsified":[99],"degrades":[101],"significantly":[102],"when":[103,121,197],"sparsity":[106,124,200],"goes":[107],"beyond":[108],"certain":[110],"extent.":[111],"Therefore,":[112],"propose":[114],"two":[115],"techniques":[116],"improve":[118],"is":[125,201],"high.":[126,202],"First,":[127],"loss":[135,156],"formulation":[136],"which,":[137],"however,":[138],"does":[139],"not":[140],"involve":[142],"all":[143],"elements":[144],"matrix;":[148],"in":[149],"contrast,":[150],"add":[152],"new":[154],"auxiliary":[155],"head":[157],"better":[159],"guide":[160],"edge":[162],"pruning":[163,183],"by":[164,171],"involving":[165],"entire":[167],"matrix.":[169],"Second,":[170],"regarding":[172],"unfavorable":[173],"sparsification":[175,244],"as":[176,185],"adversarial":[177],"data":[178],"perturbations,":[179],"formulate":[181],"process":[184],"min-max":[187],"optimization":[188],"problem":[189],"gain":[191],"robustness":[193],"lottery":[195,229],"tickets":[196],"We":[203,223],"further":[204],"investigate":[205],"question:":[207],"Can":[208],"\"retrainable\"":[210],"be":[216],"also":[217],"effective":[218],"transferring":[221],"learning?":[222],"call":[224],"it":[225],"transferable":[227,253],"(GLT)":[231],"hypothesis.":[232,255],"Extensive":[233],"experiments":[234],"were":[235],"conducted":[236],"which":[237,249],"demonstrate":[238],"superiority":[240],"our":[242,252],"proposed":[243],"method":[245],"over":[246],"empirically":[250],"verified":[251],"GLT":[254]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}
