December 06, 2021
In this paper, we theoretically characterize graph neural network’s representation power for high-order node set prediction problems (where a prediction is made over a set of more than 1 node). In particular, we focus on one most important second-order task—link prediction. There are two representative classes of GNN methods for link prediction: GAE and SEAL. GAE (Graph Autoencoder) first applies a GNN to the whole graph, and then aggregates the representations of the source and target nodes as their link representation. SEAL extracts a subgraph around the source and target nodes, labels the nodes in the subgraph, and then uses a GNN to learn a link representation from the labeled subgraph. At first glance, both GAE and SEAL use a GNN. However, their performance gap can be very large. On the recent Open Graph Benchmark datasets, SEAL achieved 3 first places out of 4 datasets, outperforming the best GAE method by up to 195% in Hits@100. In this paper, by studying this performance gap between GAE and SEAL, we first point out a key limitation of GAE caused by directly aggregating two node representations as a link representation. To address this limitation, we propose the labeling trick. Labeling trick unifies several recent successes to improve GNNs’ representation power, such as SEAL, Distance Encoding, and Identity-aware GNN, into a single and most basic form. We prove that with labeling trick a sufficiently expressive GNN can learn the most expressive structural representations for node sets. Our work establishes a theoretical foundation for using GNNs for high-order node set prediction.
Publisher
NeurIPS
Research Topics
Core Machine Learning
May 07, 2024
Hwanwoo Kim, Xin Zhang, Jiwei Zhao, Qinglong Tian
May 07, 2024
April 04, 2024
Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo
April 04, 2024
March 28, 2024
Vitoria Barin Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent
March 28, 2024
March 13, 2024
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian
March 13, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models