In the last few years, the interest in knowledge bases has grown exponentially in both the
research community and the industry due to their essential role in AI applications. Entity …

Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …

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

The user identity linkage that establishes correspondence between users across networks is
a fundamental issue in various social network applications. Efforts have recently been …

A recent study has shown that graph matching models are vulnerable to adversarial
manipulation of their input which is intended to cause a mismatching. Nevertheless, there is …

Contrastive learning has emerged as a premier method for learning representations with or
without supervision. Recent studies have shown its utility in graph representation learning …

The convergence of extremely high levels of hardware concurrency and the effective overlap
of computation and communication in asynchronous executions has resulted in increasing …

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 …

Network alignment, or the task of finding corresponding nodes in different networks, is an
important problem formulation in many application domains. We propose CAPER, a …

Graph neural networks have been widely used for learning representations of nodes for
many downstream tasks on graph data. Existing models were designed for the nodes on a …