{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104857"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104857","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Harnessing heterogeneous association in real-world networks","abstract":"Real-world networks often contain heterogeneity due to the heterogeneous nature of the world. A few examples of such networks include multi-view social networks, heterogeneous bibliographic networks, biomedical networks, etc. Ostensibly the heterogeneity of real-world network appears as the typed essence of nodes and edges. By considering type information, researchers have shown that using the typed networks can achieve performance better than using the homogeneous networks in a wide variety of downstream applications such as classification, clustering, recommendation, and outlier detection. Beyond the low-level heterogeneity in nodes and edges on the surface, their types also naturally induce higher-level typed network components. In my practice mining real-world networks, I identify that the heterogeneity also prevalently lies in the association across different network components, and such heterogeneous association is often important and intrinsic to the information embodied in the networks. In this dissertation, I investigate the necessity of modeling heterogeneous association in real-world networks and develop methodologies to simultaneously leverage the rich information and accommodate the incompatibility in the presence of heterogeneous association. A series of new models along this line are proposed for specific problems including learning network embedding, defining relevance measures, and discovering hypernymy relation, together with the discussion on how the principles reflected by these models can be used in other network mining tasks. These proposed models cannot only achieve better quantitative results but also uncover the semantics hidden in the heterogeneous association of real-world data.","abstract_html":"Real-world networks often contain heterogeneity due to the heterogeneous nature of the world. A few examples of such networks include multi-view social networks, heterogeneous bibliographic networks, biomedical networks, etc. Ostensibly the heterogeneity of real-world network appears as the typed essence of nodes and edges. By considering type information, researchers have shown that using the typed networks can achieve performance better than using the homogeneous networks in a wide variety of downstream applications such as classification, clustering, recommendation, and outlier detection. Beyond the low-level heterogeneity in nodes and edges on the surface, their types also naturally induce higher-level typed network components. In my practice mining real-world networks, I identify that the heterogeneity also prevalently lies in the association across different network components, and such heterogeneous association is often important and intrinsic to the information embodied in the networks. In this dissertation, I investigate the necessity of modeling heterogeneous association in real-world networks and develop methodologies to simultaneously leverage the rich information and accommodate the incompatibility in the presence of heterogeneous association. A series of new models along this line are proposed for specific problems including learning network embedding, defining relevance measures, and discovering hypernymy relation, together with the discussion on how the principles reflected by these models can be used in other network mining tasks. These proposed models cannot only achieve better quantitative results but also uncover the semantics hidden in the heterogeneous association of real-world data.","abstract_has_math":false,"creators":["Shi, Yu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei","Sundaram, Hari","Peng, Jian","Kim, Myunghwan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T19:55:43Z","date_published":"2019-08-23T19:55:43Z","updated_at":"2026-07-22T22:24:42Z","subjects":["Network mining","Heterogeneity","Heterogeneous association","Real-world networks"],"languages":["en"],"rights":["Copyright 2019 Yu Shi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104857","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei","Sundaram, Hari","Peng, Jian","Kim, Myunghwan"]},{"key":"dc:creator","label":"Author","values":["Shi, Yu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T19:55:43Z","2019-04-17","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Network mining","Heterogeneity","Heterogeneous association","Real-world networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Yu Shi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104857"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Real-world networks often contain heterogeneity due to the heterogeneous nature of the world. A few examples of such networks include multi-view social networks, heterogeneous bibliographic networks, biomedical networks, etc. Ostensibly the heterogeneity of real-world network appears as the typed essence of nodes and edges. By considering type information, researchers have shown that using the typed networks can achieve performance better than using the homogeneous networks in a wide variety of downstream applications such as classification, clustering, recommendation, and outlier detection. Beyond the low-level heterogeneity in nodes and edges on the surface, their types also naturally induce higher-level typed network components. In my practice mining real-world networks, I identify that the heterogeneity also prevalently lies in the association across different network components, and such heterogeneous association is often important and intrinsic to the information embodied in the networks. In this dissertation, I investigate the necessity of modeling heterogeneous association in real-world networks and develop methodologies to simultaneously leverage the rich information and accommodate the incompatibility in the presence of heterogeneous association. A series of new models along this line are proposed for specific problems including learning network embedding, defining relevance measures, and discovering hypernymy relation, together with the discussion on how the principles reflected by these models can be used in other network mining tasks. These proposed models cannot only achieve better quantitative results but also uncover the semantics hidden in the heterogeneous association of real-world data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Yu Shi, accepted the attached license on 2019-04-17 at 11:35.","The student, Yu Shi, submitted this Dissertation for approval on 2019-04-17 at 11:45.","This Dissertation was approved for publication on 2019-04-17 at 14:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13710 on 2019-08-22 at 14:44:32","Made available in DSpace on 2019-08-23T19:55:43Z (GMT). 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By considering type information, researchers have shown that using the typed networks can achieve performance better than using the homogeneous networks in a wide variety of downstream applications such as classification, clustering, recommendation, and outlier detection. Beyond the low-level heterogeneity in nodes and edges on the surface, their types also naturally induce higher-level typed network components. In my practice mining real-world networks, I identify that the heterogeneity also prevalently lies in the association across different network components, and such heterogeneous association is often important and intrinsic to the information embodied in the networks. In this dissertation, I investigate the necessity of modeling heterogeneous association in real-world networks and develop methodologies to simultaneously leverage the rich information and accommodate the incompatibility in the presence of heterogeneous association. A series of new models along this line are proposed for specific problems including learning network embedding, defining relevance measures, and discovering hypernymy relation, together with the discussion on how the principles reflected by these models can be used in other network mining tasks. These proposed models cannot only achieve better quantitative results but also uncover the semantics hidden in the heterogeneous association of real-world data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Yu Shi, accepted the attached license on 2019-04-17 at 11:35.","The student, Yu Shi, submitted this Dissertation for approval on 2019-04-17 at 11:45.","This Dissertation was approved for publication on 2019-04-17 at 14:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13710 on 2019-08-22 at 14:44:32","Made available in DSpace on 2019-08-23T19:55:43Z (GMT). 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