{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125571"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125571","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Empowering graph intelligence via natural and artificial dynamics","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Fu, Dongqi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["He, Jingrui","Abdelzaher, Tarek","Han, Jiawei","Maciejewski, Ross"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-08","date_published":"2024-07-08","updated_at":"2026-07-22T22:25:02Z","subjects":["Graph Deep Learning","Graph Machine Learning","Graph Data Mining","Graph Ai"],"languages":["en","eng"],"rights":["Copyright 2024 Dongqi Fu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125571","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["He, Jingrui","Abdelzaher, Tarek","Han, Jiawei","Maciejewski, Ross"]},{"key":"dc:creator","label":"Author","values":["Fu, Dongqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-08","2024-08"]},{"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 Deep Learning","Graph Machine Learning","Graph Data Mining","Graph Ai"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Dongqi Fu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125571"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Dongqi Fu, accepted the attached license on 2024-07-05 at 12:44.","The student, Dongqi Fu, submitted this Dissertation for approval on 2024-07-05 at 12:58.","This Dissertation was approved for publication on 2024-07-08 at 14:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20959 on 2025-02-04 at 21:04:17","In the era of big data, the relationship between entities has become much more complex than ever before. As a kind of relational data structure, graph attracts much research attention for dealing with this unprecedented phenomenon. The real-world scenarios usually bring two fundamental and pragmatic challenges to graph research. First, the graph structure and features may be complex over time (i.e., time-evolving topological structures, time-evolving node/graph features/labels, etc.). Without proper time information leverage, the resulting problems include but are not limited to ignoring entity temporal correlation, overlooking causality discovery, computation inefficiency, non-generalization, etc. Second, the initial topological structure and node or graph features may be imperfect (e.g., having construction errors, sampling noises, missing features, scarce labels, hard-to-interpret, redundant, privacy-leaking, robustness-lacking, etc.). The corresponding problems include but are not limited to non-robustness, indiscriminative representations, and non-generalization. Inspired by the above two kinds of problems, my research focuses on natural dynamics and artificial dynamics for graphs. Natural dynamics can be illustrated as disentangling the spatial-temporal complexity of input graphs with evolving components, e.g., the topology structures and (sub)graph-level features are dependent on time. As for artificial dynamics in graphs, this concept describes how researchers change the existing or construct the non-existing graph-related elements, e.g., graph topology, node/graph attributes, graph neural network (GNN) layer connections, and GNN gradients. In general, studying natural dynamics and artificial dynamics is investigating how to leverage spatial-temporal properties of graphs and augment and prune graph components to upgrade graph-based AI performance in terms of effectiveness, efficiency, trustworthiness, etc. In this thesis, we propose to build the algorithmic foundation for the next-generation graph AI development with three main pillars, i.e., natural dynamics of graphs, artificial dynamics of graphs, and \\natural + artificial dynamics of graphs. For example, to name a few, (1) we first finished a literature review for the natural and artificial dynamics of graphs in terms of concept, progress, and future; (2) by studying natural dynamics, we developed more accurate graph classification and more efficient graph alignment algorithms; (3) by studying artificial dynamics, we have developed the explainable node and graph classification algorithms and a powerful graph neural computational framework; (4) by studying natural + artificial dynamics, we obtained efficient algorithms for high-order graph clustering and partitioning algorithms."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Empowering graph intelligence via natural and artificial dynamics"]}]}],"canonical_facts":{"dc:contributor":["He, Jingrui","Abdelzaher, Tarek","Han, Jiawei","Maciejewski, Ross"],"dc:creator":["Fu, Dongqi"],"dc:date":["2024-07-08","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Dongqi Fu, accepted the attached license on 2024-07-05 at 12:44.","The student, Dongqi Fu, submitted this Dissertation for approval on 2024-07-05 at 12:58.","This Dissertation was approved for publication on 2024-07-08 at 14:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20959 on 2025-02-04 at 21:04:17","In the era of big data, the relationship between entities has become much more complex than ever before. As a kind of relational data structure, graph attracts much research attention for dealing with this unprecedented phenomenon. The real-world scenarios usually bring two fundamental and pragmatic challenges to graph research. First, the graph structure and features may be complex over time (i.e., time-evolving topological structures, time-evolving node/graph features/labels, etc.). Without proper time information leverage, the resulting problems include but are not limited to ignoring entity temporal correlation, overlooking causality discovery, computation inefficiency, non-generalization, etc. Second, the initial topological structure and node or graph features may be imperfect (e.g., having construction errors, sampling noises, missing features, scarce labels, hard-to-interpret, redundant, privacy-leaking, robustness-lacking, etc.). The corresponding problems include but are not limited to non-robustness, indiscriminative representations, and non-generalization. Inspired by the above two kinds of problems, my research focuses on natural dynamics and artificial dynamics for graphs. Natural dynamics can be illustrated as disentangling the spatial-temporal complexity of input graphs with evolving components, e.g., the topology structures and (sub)graph-level features are dependent on time. As for artificial dynamics in graphs, this concept describes how researchers change the existing or construct the non-existing graph-related elements, e.g., graph topology, node/graph attributes, graph neural network (GNN) layer connections, and GNN gradients. In general, studying natural dynamics and artificial dynamics is investigating how to leverage spatial-temporal properties of graphs and augment and prune graph components to upgrade graph-based AI performance in terms of effectiveness, efficiency, trustworthiness, etc. In this thesis, we propose to build the algorithmic foundation for the next-generation graph AI development with three main pillars, i.e., natural dynamics of graphs, artificial dynamics of graphs, and \\natural + artificial dynamics of graphs. For example, to name a few, (1) we first finished a literature review for the natural and artificial dynamics of graphs in terms of concept, progress, and future; (2) by studying natural dynamics, we developed more accurate graph classification and more efficient graph alignment algorithms; (3) by studying artificial dynamics, we have developed the explainable node and graph classification algorithms and a powerful graph neural computational framework; (4) by studying natural + artificial dynamics, we obtained efficient algorithms for high-order graph clustering and partitioning algorithms."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125571"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Dongqi Fu"],"dc:subject":["Graph Deep Learning","Graph Machine Learning","Graph Data Mining","Graph Ai"],"dc:title":["Empowering graph intelligence via natural and artificial dynamics"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}