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 …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

The current success of entity alignment (EA) is still mainly based on large-scale labeled
anchor links. However, the refined annotation of anchor links still consumes a lot of …

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 …

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 …

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

Privacy concerns on social networks have received extensive attention in recent years. The
task of user identity linkage (UIL), which aims to identify corresponding users across different …

In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a
single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) …

Graph alignment, also known as network alignment, has many applications in data mining
tasks. It aims to find the node correspondence across disjoint graphs. With recent …

Network alignment aims to identify the corresponding nodes belonging to the same entity
across different networks, which is a fundamental task in various applications. Existing …