Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification

TL;DR

This paper introduces DGASN, a novel domain-adaptive graph attention network designed for cross-network edge classification, effectively distinguishing homophilous and heterophilous edges across different networks, and demonstrating state-of-the-art results.

Contribution

The paper pioneers the study of cross-network edge classification and proposes DGASN, which combines multi-head GAT, supervised attention learning, and adversarial domain adaptation.

Findings

DGASN outperforms existing methods on benchmark datasets.

Effective discrimination between homophilous and heterophilous edges.

Successful knowledge transfer across different networks.

Abstract

Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC), and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. Firstly, DGASN adopts multi-head GAT as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.