Publication Date
12-2024
Date of Final Oral Examination (Defense)
10-11-2024
Type of Culminating Activity
Dissertation
Degree Title
Doctor of Philosophy in Computing
Department
Computer Science
Supervisory Committee Chair
Edoardo Serra, Ph.D.
Supervisory Committee Member
Francesca Spezzano, Ph.D.
Supervisory Committee Member
Sole Pera, Ph.D.
Supervisory Committee Member
Marion Scheepers, Ph.D.
Abstract
Graphs, i.e., sets of entities (nodes) linked to one other (via edges), represent a universal data structure to describe interacting entities. Machine learning tasks on graphs are enabled by Graph Representation Learning, which automates the task of calculating numerical vector representations for components of a graph (nodes, edges, subgraphs, entire graphs) that preserve relationship information. Structural graph representation learning (e.g. for nodes) aims to encode information about the structure of a node's neighborhood, rather than the identity of its connected nodes. Few works have developed efficient and effective structural graph representation learning approaches for dynamic graphs. This work introduces Temporal SIR-GN, which calculates node representations based upon the probabilities that their neighborhood structures transition from one type to another over time. The time complexity for this algorithm is linear in the number of timestamps in a graph, and demonstrates drastically shorter runtimes than the state-of-the-art. In addition, Temporal SIR-GN outperforms existing SOTA at capturing temporal structure, resulting in better performance at node classification tasks. Capitalizing on the efficiency and performance of Temporal SIR-GN, a high-order temporal method Arbitrary-Order Temporal SIR-GN was developed to capture structural evolution in terms of extended (rather than pairwise) transitions. This higher-order method, while still efficient, outperforms even Temporal SIR-GN on node classification tasks. Finally, important modifications to Temporal SIR-GN enabled its use in a streaming approach, that is, to update incrementally as the graph is changing. Together, these works provide propitious advancements to the field of graph representation learning for dynamic graphs.
DOI
https://doi.org/10.18122/td.2292.boisestate
Recommended Citation
Layne, Janet, "Advancing Structural Representation Learning for Static and Dynamic Graphs" (2024). Boise State University Theses and Dissertations. 2292.
https://doi.org/10.18122/td.2292.boisestate