{"id":"https://openalex.org/W2219485415","doi":"https://doi.org/10.1109/bigdata.2015.7363805","title":"Scalable classification for large dynamic networks","display_name":"Scalable classification for large dynamic networks","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2219485415","doi":"https://doi.org/10.1109/bigdata.2015.7363805","mag":"2219485415"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","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/A5024333188","display_name":"Yibo Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yibo Yao","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044712355","display_name":"Lawrence B. Holder","orcid":"https://orcid.org/0000-0002-6586-3144"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence B. Holder","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024333188"],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":0.4772,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66625387,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"2","issue":null,"first_page":"609","last_page":"618"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9894000291824341,"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/T12761","display_name":"Data Stream Mining Techniques","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/computer-science","display_name":"Computer science","score":0.7698346376419067},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5768632292747498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5465109944343567},{"id":"https://openalex.org/keywords/graph-kernel","display_name":"Graph kernel","score":0.5215868353843689},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5051661133766174},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4591353237628937},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43562060594558716},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.43422842025756836},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39558330178260803},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39069876074790955},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.34843337535858154},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3402699828147888},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.29531586170196533},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.27529239654541016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7698346376419067},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5768632292747498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465109944343567},{"id":"https://openalex.org/C100595998","wikidata":"https://www.wikidata.org/wiki/Q11731931","display_name":"Graph kernel","level":5,"score":0.5215868353843689},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5051661133766174},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4591353237628937},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43562060594558716},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.43422842025756836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39558330178260803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39069876074790955},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.34843337535858154},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3402699828147888},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29531586170196533},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.27529239654541016},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7799999713897705,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W111739550","https://openalex.org/W155053533","https://openalex.org/W1501260812","https://openalex.org/W1816257748","https://openalex.org/W1835509607","https://openalex.org/W1973678258","https://openalex.org/W2004109568","https://openalex.org/W2027840186","https://openalex.org/W2080554241","https://openalex.org/W2084689094","https://openalex.org/W2103736488","https://openalex.org/W2107410045","https://openalex.org/W2121423746","https://openalex.org/W2124995446","https://openalex.org/W2142498761","https://openalex.org/W2147286743","https://openalex.org/W2150621701","https://openalex.org/W2153635508","https://openalex.org/W2159156271","https://openalex.org/W2160218441","https://openalex.org/W2399708603","https://openalex.org/W2404666544","https://openalex.org/W2911738047","https://openalex.org/W3009009611","https://openalex.org/W6606307724","https://openalex.org/W6629795027","https://openalex.org/W6638258969","https://openalex.org/W6675774749","https://openalex.org/W6676388157","https://openalex.org/W6681029592","https://openalex.org/W6683584131","https://openalex.org/W6713411369","https://openalex.org/W6758327135","https://openalex.org/W7065437122"],"related_works":["https://openalex.org/W1487808658","https://openalex.org/W2043334680","https://openalex.org/W3099811568","https://openalex.org/W2078320823","https://openalex.org/W4285213554","https://openalex.org/W3008854474","https://openalex.org/W2098940859","https://openalex.org/W4401177601","https://openalex.org/W3044028388","https://openalex.org/W1515878412"],"abstract_inverted_index":{"We":[0,30,82],"examine":[1],"the":[2,25,43,56,71,75,84],"problem":[3],"of":[4,35,50,74,86],"node":[5],"classification":[6],"in":[7],"large-scale":[8],"and":[9,69],"dynamically":[10],"changing":[11],"graphs.":[12],"An":[13],"entropy-based":[14],"subgraph":[15],"extraction":[16],"method":[17],"has":[18],"been":[19],"developed":[20],"for":[21,46],"extracting":[22],"subgraphs":[23],"surrounding":[24],"nodes":[26,77],"to":[27,40,64],"be":[28],"classified.":[29],"introduce":[31],"an":[32,36],"online":[33],"version":[34],"existing":[37],"graph":[38,97],"kernel":[39,44,57,62],"incrementally":[41],"compute":[42],"matrix":[45],"a":[47,61,66],"unbounded":[48],"stream":[49],"these":[51],"extracted":[52],"subgraphs.":[53,81],"After":[54],"obtaining":[55],"values,":[58],"we":[59],"adopt":[60],"perceptron":[63],"learn":[65],"discriminative":[67],"classifier":[68],"predict":[70],"class":[72],"labels":[73],"target":[76],"with":[78],"their":[79],"corresponding":[80],"demonstrate":[83],"advantages":[85],"our":[87],"learning":[88],"techniques":[89],"by":[90],"conducting":[91],"empirical":[92],"evaluations":[93],"on":[94],"two":[95],"real-world":[96],"datasets.":[98]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}