Multiple networks emerge in a wealth of high-impact applications. Network alignment, which
aims to find the node correspondence across different networks, plays a fundamental role for …

Network alignment is a critical steppingstone behind a variety of multi-network mining tasks.
Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based …

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

Network biology, an interdisciplinary field at the intersection of computational and biological
sciences, is critical for deepening understanding of cellular functioning and disease. While …

Nowadays, it is common for a person to possess different identities on multiple social
platforms. Social network alignment aims to match the identities that from different networks …

Network alignment finds node correspondences across multiple networks, where the
alignment accuracy is of crucial importance because of its profound impact on downstream …

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

Finding node associations across different networks is the cornerstone behind a wealth of
high-impact data mining applications. Traditional approaches are often, explicitly or …

Network alignment, or the task of finding meaningful node correspondences between nodes
in different graphs, is an important graph mining task with many scientific and industrial …

Networks are prevalent in many areas and are often collected from multiple sources.
However, due to the veracity characteristics, more often than not, networks are incomplete …