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oai:www.ideals.illinois.edu:2142/125571 2025-02-06 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text 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 Empowering graph intelligence via natural and artificial dynamics Fu, Dongqi 2024-07-08 He, Jingrui He, Jingrui Abdelzaher, Tarek Han, Jiawei Maciejewski, Ross Graph Deep Learning Graph Machine Learning Graph Data Mining Graph Ai eng 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. 2024-08 Thesis https://hdl.handle.net/2142/125571 Copyright 2024 Dongqi Fu Siebel Computing &DataScience Computer Science University of Illinois at Urbana-Champaign Ph.D. Dissertation