{"id":"https://openalex.org/W3159799347","doi":"https://doi.org/10.1109/icpr48806.2021.9413216","title":"Region and Relations Based Multi Attention Network for Graph Classification","display_name":"Region and Relations Based Multi Attention Network for Graph Classification","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3159799347","doi":"https://doi.org/10.1109/icpr48806.2021.9413216","mag":"3159799347"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413216","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","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/A5020832986","display_name":"Manasvi Aggarwal","orcid":"https://orcid.org/0009-0003-0433-6716"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manasvi Aggarwal","raw_affiliation_strings":["Myntra, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Myntra, Bangalore, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028996052","display_name":"M. Narasimha Murty","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. N. Murty","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2799,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62451452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"8101","last_page":"8108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9901999831199646,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9857000112533569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.875184178352356},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7368248701095581},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5890685319900513},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5362038016319275},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5347439646720886},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5080416202545166},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43057963252067566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4229843020439148},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.4116554260253906},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3328269124031067},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18269437551498413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1533249318599701}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.875184178352356},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7368248701095581},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5890685319900513},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5362038016319275},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5347439646720886},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5080416202545166},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43057963252067566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4229843020439148},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.4116554260253906},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3328269124031067},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18269437551498413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1533249318599701},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413216","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.iisc.ac.in:69709","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401429","display_name":"ePrints@IISc (Indian Institute of Science)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I59270414","host_organization_name":"Indian Institute of Science Bangalore","host_organization_lineage":["https://openalex.org/I59270414"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1662382123","https://openalex.org/W1899504021","https://openalex.org/W2008857988","https://openalex.org/W2016053056","https://openalex.org/W2142498761","https://openalex.org/W2147286743","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2159156271","https://openalex.org/W2160815625","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2558460151","https://openalex.org/W2606202972","https://openalex.org/W2606780347","https://openalex.org/W2726670313","https://openalex.org/W2756815061","https://openalex.org/W2787420051","https://openalex.org/W2788919350","https://openalex.org/W2789331392","https://openalex.org/W2809146571","https://openalex.org/W2809343047","https://openalex.org/W2811124557","https://openalex.org/W2899379687","https://openalex.org/W2911738047","https://openalex.org/W2939208918","https://openalex.org/W2942681667","https://openalex.org/W2950898568","https://openalex.org/W2951659295","https://openalex.org/W2952832237","https://openalex.org/W2962711740","https://openalex.org/W2962756421","https://openalex.org/W2962767366","https://openalex.org/W2962810718","https://openalex.org/W2962867885","https://openalex.org/W2963175980","https://openalex.org/W2963403868","https://openalex.org/W2963711743","https://openalex.org/W2963716836","https://openalex.org/W2963858333","https://openalex.org/W2963893572","https://openalex.org/W2964015378","https://openalex.org/W2964113829","https://openalex.org/W2964121744","https://openalex.org/W2964145825","https://openalex.org/W2964311892","https://openalex.org/W2964321699","https://openalex.org/W2970493342","https://openalex.org/W2994710732","https://openalex.org/W3104097132","https://openalex.org/W4285723986","https://openalex.org/W4288275971","https://openalex.org/W4288347098","https://openalex.org/W4288363255","https://openalex.org/W4294170691","https://openalex.org/W4294558607","https://openalex.org/W4300537282","https://openalex.org/W4385245566","https://openalex.org/W6681029592","https://openalex.org/W6682691769","https://openalex.org/W6684191040","https://openalex.org/W6685350579","https://openalex.org/W6713582119","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6736685754","https://openalex.org/W6738964360","https://openalex.org/W6739901393","https://openalex.org/W6748524405","https://openalex.org/W6752737978","https://openalex.org/W6753331806","https://openalex.org/W6754929296","https://openalex.org/W6758327135","https://openalex.org/W6761665040","https://openalex.org/W6762453230","https://openalex.org/W6766156693","https://openalex.org/W7065437122"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W3015684221"],"abstract_inverted_index":{"Graphs":[0],"are":[1,48],"non-euclidean":[2,23],"structures":[3],"that":[4,169],"can":[5],"represent":[6],"many":[7],"relational":[8],"data":[9],"efficiently.":[10],"Many":[11],"studies":[12],"have":[13,29],"proposed":[14,141],"the":[15,18,22,59,62,66,81,85,90,127,162,172,176],"convolution":[16,27],"and":[17,40,61],"pooling":[19,46,76],"operators":[20,28],"on":[21,33,181],"domain.":[24],"The":[25],"graph":[26,43,75,179],"shown":[30],"astounding":[31],"performance":[32,177],"various":[34,182],"tasks":[35],"such":[36],"as":[37,87,89],"node":[38,60,86],"representation":[39],"classification.":[41],"For":[42,123],"classification,":[44],"different":[45],"techniques":[47],"introduced,":[49],"but":[50],"none":[51],"of":[52,58,65,129,178],"them":[53],"has":[54,171],"considered":[55],"both":[56],"neighborhood":[57],"long-range":[63],"dependencies":[64,91],"node.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71,97,125],"propose":[72,98,126],"a":[73,99],"novel":[74],"layer":[77,156],"R2POOL,":[78],"which":[79],"balances":[80],"structure":[82],"information":[83],"around":[84],"well":[88],"with":[92],"far":[93],"away":[94],"nodes.":[95],"Further,":[96],"new":[100],"training":[101,143,147],"strategy":[102],"to":[103,106,116,174],"learn":[104],"coarse":[105],"fine":[107],"representations.":[108],"We":[109,160],"add":[110],"supervision":[111],"at":[112,154],"only":[113,120],"intermediate":[114],"levels":[115],"generate":[117],"predictions":[118],"using":[119],"intermediate-level":[121],"features.":[122],"this,":[124],"concept":[128],"an":[130],"alignment":[131],"score.":[132],"Moreover,":[133],"each":[134,155],"layer's":[135],"prediction":[136],"is":[137],"controlled":[138],"by":[139,165],"our":[140],"branch":[142],"strategy.":[144],"This":[145],"complete":[146],"helps":[148],"in":[149],"learning":[150],"dominant":[151],"class":[152],"features":[153],"for":[157],"representing":[158],"graphs.":[159],"call":[161],"combined":[163],"model":[164],"R2MAN.":[166],"Experiments":[167],"show":[168],"R2MAN":[170],"potential":[173],"improve":[175],"classification":[180],"datasets.":[183]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}