Beyond Message Passing Paradigm: Training Graph Data with Consistency Constraints
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on powerful message passing to guide feature aggregation among neighbors. Despite their success, there still exist three weaknesses that limit their capacity to train graph data: weak generalization with severely limited labeled data, poor robustness to label noise and structure disturbation, and high computation and memory burden for keeping the entire graph. In this paper, we propose a simple yet effective Graph Consistency Learning (GCL) framework, which is based purely on multilayer perceptrons, where structure information is only implicitly incorporated as prior knowledge in the computation of supervision signals but does not explicitly involve the forward. Specifically, the GCL framework is optimized with three well-designed consistency constraints: neighborhood consistency, label consistency, and class-center consistency. More importantly, we provide theoretical analysis on the connections between message passing and consistency constraints. Extensive experiments show that GCL produces truly encouraging performance with better generalization and robustness compared with other leading methods.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Message passing all the way up
The message passing framework is the foundation of the immense success enjoyed by graph neural networks (GNNs) in recent years. In spite of its elegance, there exist many problems it provably cannot solve over given inpu…
AllGraph Representation LearningRepresentation LearningPANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-ra…
Graph ClassificationGraph Neural NetworkGraph RegressionNode ClassificationHypergraph Node Representation Learning with One-Stage Message Passing
Hypergraphs as an expressive and general structure have attracted considerable attention from various research domains. Most existing hypergraph node representation learning techniques are based on graph neural networks,…
Representation LearningGraph Coarsening with Message-Passing Guarantees
Graph coarsening aims to reduce the size of a large graph while preserving some of its key properties, which has been used in many applications to reduce computational load and memory footprint. For instance, in graph ma…
Node ClassificationCooperative Graph Neural Networks
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph n…