5th Year Master's Thesis Presentation - Tongzhou Thomas Liao

— 4:00pm

Location:
In Person - Gates Hillman 7101

Speaker:
TONGZHOU THOMAS LIAO , Master's Student, Computer Science Department, Carnegie Mellon University

Enhancing GNNs with Encoding, Rewiring, and Attention

Graph Neural Networks (GNNs) have become important tools for machine learning on graph-structured data. In this paper, we explore the synergistic combination of graph encoding, graph rewiring, and graph attention, by introducing Graph Attention with Stochastic Structures (GRASS), a novel GNN architecture. GRASS utilizes relative random walk probabilities (RRWP) encoding and a novel decomposed variant (D-RRWP) to efficiently capture structural information. It rewires the input graph by superimposing a random regular graph to enhance long-range information propagation. It also employs a novel additive attention mechanism tailored for graph-structured data. Our empirical evaluations demonstrate that GRASS achieves state-of-the-art performance on multiple benchmark datasets, including a 20.3% reduction in mean absolute error on the ZINC dataset. 

Thesis Committee

Barnabás Póczos (Chair)
Tianqi Chen

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