How Powerful are Graph Neural Networks?

Jan 22, 2021 · 2 min read
seminars

Event

Weekly OSI Lab Seminar

Short summary

In this seminar, I will talk about the influential paper “How Powerful are Graph Neural Networks?” (Xu et al., ICLR'19 oral).

Abstract

(taken directly from the paper)

Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and trans- forming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.

Papers

Papers discussed in the seminar:

  • Main: Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. How Powerful are Graph Neural Networks?. In ICLR 2019. (Oral!)
Junghyun Lee
Authors
Junghyun Lee
PhD Candidate in Artificial Intelligence
PhD candidate at KAIST AI, jointly advised by Se-Young Yun and Chulhee Yun. I work on interactive machine learning, theoretical aspects of LLMs, learning/optimization theory, and statistical analysis of large networks.