In this work, we investigate the user identity linkage task across different social media
platforms based on heterogeneous multi-modal posts and social connections. This task is …

Despite achieving remarkable performance, deep graph learning models, such as node
classification and network embedding, suffer from harassment caused by small adversarial …

User identity linkage (UIL) across social networks has recently attracted an increasing
amount of attention due to its significant research challenges and practical value. Most of the …

Recent years have witnessed increasing attention on the application of graph alignment to
on-Web tasks, such as knowledge graph integration and social network linking. Despite …

Graph alignment, which aims at identifying corresponding entities across multiple networks,
has been widely applied in various domains. As the graphs to be aligned are usually …

The human-oriented applications aim to exploit behaviors of people, which impose
challenges on user modeling of integrating social network (SN) with knowledge graph (KG) …

Network alignment, which aims at learning a matching between the same entities across
multiple information networks, often suffers challenges from feature inconsistency, high …

Recent studies have shown that graph learning models are highly vulnerable to adversarial
attacks, and network alignment methods are no exception. How to enhance the robustness …

Network alignment is a critical task in a wide variety of fields. Many existing works leverage
on representation learning to accomplish this task without eliminating domain representation …

User identity linkage (UIL) task aims to infer the identical users between different social
networks/platforms. Existing models leverage the labeled inter-linkages or high-quality user …