{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109388"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109388","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multi-facet graph mining with contextualized projections","abstract":"The goal of my doctoral research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various domains. Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. To overcome such barriers, my research harnesses the power of multi-facet context modeling and focuses on the principle of contextualized projections, which provides generic but subtle solutions to knowledge discovery over graphs with the mixtures of various semantic contexts.","abstract_html":"The goal of my doctoral research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various domains. Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. To overcome such barriers, my research harnesses the power of multi-facet context modeling and focuses on the principle of contextualized projections, which provides generic but subtle solutions to knowledge discovery over graphs with the mixtures of various semantic contexts.","abstract_has_math":false,"creators":["Yang, Carl"],"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","Peng, Jian","Zhai, ChengXiang","Leskovec, Jure"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:38:06Z","date_published":"2021-03-05T21:38:06Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Graph mining","Contextualized projection","Graph embedding","Graph generation","Network applications"],"languages":["en"],"rights":["Copyright 2020 Ji Yang (Carl)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109388","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei","Peng, Jian","Zhai, ChengXiang","Leskovec, Jure"]},{"key":"dc:creator","label":"Author","values":["Yang, Carl"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:38:06Z","2020-12-01","2020-12"]},{"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":["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":["Graph mining","Contextualized projection","Graph embedding","Graph generation","Network applications"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Ji Yang (Carl)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109388"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The goal of my doctoral research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various domains. Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. 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Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. To overcome such barriers, my research harnesses the power of multi-facet context modeling and focuses on the principle of contextualized projections, which provides generic but subtle solutions to knowledge discovery over graphs with the mixtures of various semantic contexts.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms","The student, Carl Yang, accepted the attached license on 2020-11-25 at 10:47.","The student, Carl Yang, submitted this Dissertation for approval on 2020-11-25 at 11:00.","This Dissertation was approved for publication on 2020-12-01 at 14:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15961 on 2021-03-04 at 15:35:06","Made available in DSpace on 2021-03-05T21:38:06Z (GMT). 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