Papers › Local-to-global Perspectives on Graph Neural Networks

Local-to-global Perspectives on Graph Neural Networks

11 Jun 2023arXiv:2306.06547archive 2025-07-28

Chen Cai

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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Absolute Position Encodings Adam Attention 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 Softmax Transformer

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