{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110721"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110721","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dense subgraph detection on multi-layered networks","abstract":"Dense subgraph detection is a fundamental building block for a variety of applications. Most of the existing methods aim to discover dense subgraphs within a single network, or within a multi-view network consisting of a common set of nodes. However, many real-world applications can be better modeled as multi-layered networks, where nodes and their dependencies vary across the different layers. Dense subgraph detection on such multi-layered networks can help reveal interesting patterns, but largely remains a daunting task. To this end, we propose a family of algorithms (DESTINE) to detect dense subgraphs on multi-layered networks. The key idea is based on cross-layer consistency among the dense subgraphs underlying the networks at different layers. With an optimization-based formulation, we develop the projected gradient descent algorithms that bear the following distinctive advantages. First (applicability), the model is suitable for the generally defined multi-layered networks without requirements for sharing the same set of nodes across layers or 1-on-1 cross-layer dependencies. Second (generality), our model can naturally handle various task settings, including dense subgraph detection in multi-layered bipartite scenarios and in query-specific scenarios. Third (scalability), DESTINE scales linearly w.r.t. the size of the input multi-layered networks. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithms in various scenarios.","abstract_html":"Dense subgraph detection is a fundamental building block for a variety of applications. Most of the existing methods aim to discover dense subgraphs within a single network, or within a multi-view network consisting of a common set of nodes. However, many real-world applications can be better modeled as multi-layered networks, where nodes and their dependencies vary across the different layers. Dense subgraph detection on such multi-layered networks can help reveal interesting patterns, but largely remains a daunting task. To this end, we propose a family of algorithms (DESTINE) to detect dense subgraphs on multi-layered networks. The key idea is based on cross-layer consistency among the dense subgraphs underlying the networks at different layers. With an optimization-based formulation, we develop the projected gradient descent algorithms that bear the following distinctive advantages. First (applicability), the model is suitable for the generally defined multi-layered networks without requirements for sharing the same set of nodes across layers or 1-on-1 cross-layer dependencies. Second (generality), our model can naturally handle various task settings, including dense subgraph detection in multi-layered bipartite scenarios and in query-specific scenarios. Third (scalability), DESTINE scales linearly w.r.t. the size of the input multi-layered networks. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithms in various scenarios.","abstract_has_math":false,"creators":["Xu, Zhe"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Tong, Hanghang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:43Z","date_published":"2021-09-17T02:34:43Z","updated_at":"2026-07-22T22:24:52Z","subjects":["dense subgraph detection","multi-layered network","graph mining"],"languages":["en"],"rights":["Copyright 2021 Zhe Xu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110721","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tong, Hanghang"]},{"key":"dc:creator","label":"Author","values":["Xu, Zhe"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:43Z","2023-09-17T02:34:57Z","2021-04-26","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["dense subgraph detection","multi-layered network","graph mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Zhe Xu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110721"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dense subgraph detection is a fundamental building block for a variety of applications. Most of the existing methods aim to discover dense subgraphs within a single network, or within a multi-view network consisting of a common set of nodes. However, many real-world applications can be better modeled as multi-layered networks, where nodes and their dependencies vary across the different layers. Dense subgraph detection on such multi-layered networks can help reveal interesting patterns, but largely remains a daunting task. To this end, we propose a family of algorithms (DESTINE) to detect dense subgraphs on multi-layered networks. The key idea is based on cross-layer consistency among the dense subgraphs underlying the networks at different layers. With an optimization-based formulation, we develop the projected gradient descent algorithms that bear the following distinctive advantages. First (applicability), the model is suitable for the generally defined multi-layered networks without requirements for sharing the same set of nodes across layers or 1-on-1 cross-layer dependencies. Second (generality), our model can naturally handle various task settings, including dense subgraph detection in multi-layered bipartite scenarios and in query-specific scenarios. Third (scalability), DESTINE scales linearly w.r.t. the size of the input multi-layered networks. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithms in various scenarios.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Zhe Xu, accepted the attached license on 2021-04-21 at 17:09.","The student, Zhe Xu, submitted this Thesis for approval on 2021-04-21 at 17:28.","This Thesis was approved for publication on 2021-04-26 at 08:31.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16482 on 2021-09-16 at 17:04:30","Made available in DSpace on 2021-09-17T02:34:43Z (GMT). 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Most of the existing methods aim to discover dense subgraphs within a single network, or within a multi-view network consisting of a common set of nodes. However, many real-world applications can be better modeled as multi-layered networks, where nodes and their dependencies vary across the different layers. Dense subgraph detection on such multi-layered networks can help reveal interesting patterns, but largely remains a daunting task. To this end, we propose a family of algorithms (DESTINE) to detect dense subgraphs on multi-layered networks. The key idea is based on cross-layer consistency among the dense subgraphs underlying the networks at different layers. With an optimization-based formulation, we develop the projected gradient descent algorithms that bear the following distinctive advantages. First (applicability), the model is suitable for the generally defined multi-layered networks without requirements for sharing the same set of nodes across layers or 1-on-1 cross-layer dependencies. Second (generality), our model can naturally handle various task settings, including dense subgraph detection in multi-layered bipartite scenarios and in query-specific scenarios. Third (scalability), DESTINE scales linearly w.r.t. the size of the input multi-layered networks. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithms in various scenarios.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Zhe Xu, accepted the attached license on 2021-04-21 at 17:09.","The student, Zhe Xu, submitted this Thesis for approval on 2021-04-21 at 17:28.","This Thesis was approved for publication on 2021-04-26 at 08:31.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16482 on 2021-09-16 at 17:04:30","Made available in DSpace on 2021-09-17T02:34:43Z (GMT). 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