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

arXiv: https://arxiv.org/abs/2309.07380 · HTML: https://mlsanity.com/paper/2309.07380/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [Graph Domain Adaptation: Challenges, Progress and Prospects](https://mlsanity.com/paper/2402.00904/) (2024)
- [DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation](https://mlsanity.com/paper/2409.08946/) (2024)
- [A Survey of Cross-domain Graph Learning: Progress and Future Directions](https://mlsanity.com/paper/2503.11086/) (2025)
- [Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation](https://mlsanity.com/paper/2509.21059/) (2025)

## Abstract (arXiv)

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.