Local-to-global Perspectives on Graph Neural Networks
This thesis presents a local-to-global perspective on graph neural networks (GNN), the leading architecture to process graph-structured data. After categorizing GNN into local Message Passing Neural Networks (MPNN) and global Graph transformers, we present three pieces of work: 1) study the convergence property of a type of global GNN, Invariant Graph Networks, 2) connect the local MPNN and global Graph Transformer, and 3) use local MPNN for graph coarsening, a standard subroutine used in global modeling.
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Tasks
OGB
Long Range Graph Benchmark (LRGB)
OGB-LSC
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Methods
Absolute Position Encodings •
Adam •
BPE •
Dense Connections •
Dropout •
Graph Transformer •
Label Smoothing •
LapEigen •
Laplacian PE •
Layer Normalization •
Linear Layer •
MPNN •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Transformer
