Network alignment, or identifying the same entities (anchors) across multiple networks, has
significant applications across diverse fields. Unsupervised approaches for network …

Network alignment (NA) that identifies equivalent nodes across networks is an effective tool
for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter …

For exact 3D shape correspondence (matching or alignment), ie, the task of matching each
point on a shape to its exact corresponding point on the other shape (or to be more specific …

Graph learning is a prevalent domain that endeavors to learn the intricate relationships
among nodes and the topological structure of graphs. Over the years, graph learning has …

Privacy issues on social networks have been extensively discussed in recent years. The
user identity linkage (UIL) task, aiming at finding corresponding users across different social …

Social media will continue growing rapidly and integration of social media information has
become important. Information integration and many tasks require graph alignment or …

Anomaly subgraph detection is an important problem that has been well researched in
various applications, ranging from cyberattacks in computer networks to malicious activities …

Cross-social network user alignment is to find users with the same identity in multiple social
networks. It has important applications in natural and scientific fields, such as link prediction …

Anchor link identification is a task that determines which pair of accounts in different social
networks belongs to the same user. As a foundation of many applications, such as …

Networks are structures that naturally capture relations between entities in different data
sources and information systems. To establish the connections among different networks …