Knowledge graph reasoning plays a pivotal role in many real-world applications, such as
network alignment, computational fact-checking, recommendation, and many more. Among …

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

Network alignment, which aims to find the node correspondence across multiple networks, is
a fundamental task in many areas, ranging from social network analysis to adversarial …

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

Networks are prevalent and often collected from multiple sources in many high-impact
domains, which facilitate many emerging applications that require the connections across …

Online social network (OSN) applications provide different experiences; for example, posting
a short text on Twitter and sharing photographs on Instagram. Multiple OSNs constitute a …

Network alignment is useful for multiple applications that require increasingly large graphs
to be processed. Existing research approaches this as an optimization problem or computes …

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

While most network embedding techniques model the proximity between nodes in a
network, recently there has been significant interest in structural embeddings that are based …

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 …