Year: 2026
Type: article
Abstract: ABSTRACT Cross‐network node classification (CNC) aims to classify the nodes of an unlabeled graph by leveraging a graph with rich labelled nodes. The node representation and cross‐network discrepancy are two important issues in CNC. Most methods argue that the node representation is crucial to the performance of CNC and learn the representation based on the rich neighbourhood. However, in applications, the networks follow a long‐tailed distribution in their node degrees, that is, most nodes are tail nodes linked to a small amount of neighbours. The sparsity of neighbourhood challenges existing methods in representation and classification. To this end, we propose a label‐aware cross‐network node classification (LA‐CNC) method. First, a label‐consistent augmentation is designed for each network to enrich the representation by augmenting the neighbourhood of tail nodes. Second, a label contrast loss is introduced and combined with adversarial loss to enhance the distinguishability of cross‐network invariant representation. Extensive experiments demonstrate that our method outperforms state‐of‐the‐art methods on several datasets.
Source: Expert Systems
DOI: 10.1111/exsy.70212
Authors: Yuhong Zhang, Congmei Shi, Xuegang Hu
Institution: Hefei University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: G460723192
OpenAlex ID: W7125972519