Local-to-global Perspectives on Graph Neural Networks

Chen Cai

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arXiv · 2306.06547

Local-to-global Perspectives on Graph Neural Networks

Chen Cai

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Abstract

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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Chen Cai. 2023-06-11. Local-to-global Perspectives on Graph Neural Networks. https://arxiv.org/abs/2306.06547

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