{"id":"https://openalex.org/W7152520125","doi":"https://doi.org/10.1145/3774904.3792711","title":"RARD: Rationale-First Blockwise Autoregressive Diffusion in Rationale?Dominated Graph Generation","display_name":"RARD: Rationale-First Blockwise Autoregressive Diffusion in Rationale?Dominated Graph Generation","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152520125","doi":"https://doi.org/10.1145/3774904.3792711"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792711","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792711","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792711","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fan Xu","orcid":"https://orcid.org/0009-0009-0853-836X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Xu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0009-0853-836X","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yijun Zhang","orcid":"https://orcid.org/0009-0009-9828-3531"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yijun Zhang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0009-9828-3531","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sibo Zhang","orcid":"https://orcid.org/0009-0003-3495-8153"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sibo Zhang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0003-3495-8153","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiaxin Ding","orcid":"https://orcid.org/0000-0002-0009-9237"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaxin Ding","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0009-9237","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Luoyi Fu","orcid":"https://orcid.org/0000-0001-7796-9168"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luoyi Fu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-7796-9168","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xinbing Wang","orcid":"https://orcid.org/0000-0002-0357-8356"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinbing Wang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0357-8356","affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1517","last_page":"1528"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6642000079154968,"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":0.6642000079154968,"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/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.054499998688697815,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.03229999914765358,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/graph","display_name":"Graph","score":0.4661000072956085},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.3303000032901764},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3190999925136566},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.30239999294281006}],"concepts":[{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4661000072956085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4480000138282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3896999955177307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36070001125335693},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3215999901294708},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2971000075340271},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792711","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792711","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792711","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792711","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.4888615608215332,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1966878439","https://openalex.org/W1977340881","https://openalex.org/W2081681998","https://openalex.org/W2151936673","https://openalex.org/W2963028280","https://openalex.org/W3017039479","https://openalex.org/W3201230437","https://openalex.org/W4213033583","https://openalex.org/W4255383393","https://openalex.org/W4327813569","https://openalex.org/W4389524040","https://openalex.org/W4393147873","https://openalex.org/W4409365244"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"generation":[1],"underlies":[2],"many":[3,18],"critical":[4],"applications,":[5],"from":[6,95],"social":[7],"network":[8],"modeling":[9],"to":[10,53,70,82,149],"knowledge":[11],"graph":[12,86],"reasoning.":[13],"Across":[14],"these":[15,55],"diverse":[16],"domains,":[17],"graphs":[19,104],"are":[20],"rationale\u2013dominated":[21],":":[22],"a":[23,65,78,117,150],"small,":[24],"semantically":[25],"meaningful":[26],"subgraph":[27],"determines":[28],"the":[29,34,42,74,124,141],"property":[30],"of":[31,44],"interest,":[32],"while":[33,128],"remaining":[35],"edges":[36],"contribute":[37],"largely":[38],"noisy":[39],"variation.":[40],"Despite":[41],"significance":[43],"this":[45,99],"inherent":[46],"structure,":[47],"existing":[48],"generative":[49],"methods":[50],"often":[51],"fail":[52],"preserve":[54],"task\u2013critical":[56],"substructures.":[57],"We":[58,134],"introduce":[59],"RARD":[60,76,102,160],"(Rationale-first":[61],"blockwise":[62],"AutoRegressive":[63],"Diffusion),":[64],"topology-guided":[66],"framework":[67],"that":[68,91,139,159],"learns":[69],"separate":[71],"and":[72,111,147],"prioritize":[73],"rationale.":[75],"employs":[77],"persistent-homology-based":[79],"learning":[80],"objective":[81],"discover":[83],"an":[84,88],"optimal":[85],"filtration,":[87,101],"edge":[89],"ordering":[90,146],"explicitly":[92],"separates":[93],"rationale":[94,125],"noise.":[96],"Building":[97],"upon":[98],"learned":[100],"generates":[103],"blockwise:":[105],"it":[106],"adds":[107],"filtration-aligned":[108],"blocks":[109],"autoregressively":[110],"refines":[112],"each":[113],"new":[114],"block":[115],"with":[116],"shared":[118],"local":[119],"discrete":[120],"diffusion":[121],"module,":[122],"ensuring":[123],"appears":[126],"early":[127],"peripheral":[129],"structure":[130],"is":[131],"added":[132],"later.":[133],"provide":[135],"theoretical":[136],"analysis":[137],"showing":[138],"maximizing":[140],"topological":[142],"gap":[143],"yields":[144],"rationale-first":[145],"collapses":[148],"two-level":[151],"filtration.":[152],"Comprehensive":[153],"experiments":[154],"across":[155],"seven":[156],"benchmarks":[157],"demonstrate":[158],"achieves":[161],"state-of-the-art":[162],"performance":[163],"on":[164],"widely":[165],"used":[166],"metrics.":[167]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-10T00:00:00"}
