Graph alignment aims to identify node correspondence across multiple graphs, with
significant implications in various domains. As supervision information is often not available …

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

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 …

Presently with technology node scaling, an accurate prediction model at early design stages
can significantly reduce the design cycle. Especially during logic synthesis, predicting cell …

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 …

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 …

People usually interact in groups, and such groups may appear on different platforms. For
instance, people often create various group chats on messaging apps (eg, Facebook …

The primary concern of numerous online social media network (SMN) platforms is how to
provide users with effective and personalized web services. To achieve this goal, SMN …