Network alignment is the problem of pairing nodes between two graphs such that the paired
nodes are structurally and semantically similar. A well-known application of network …

Knowledge graphs (KGs) have become popular structures for unifying real-world entities by
modelling the relationships between them and their attributes. To support multilingual …

We establish a bridge between spectral clustering and Gromov-Wasserstein Learning
(GWL), a recent optimal transport-based approach to graph partitioning. This connection …

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

Recent years have witnessed increasing attention on the application of graph alignment to
on-Web tasks, such as knowledge graph integration and social network linking. Despite …

Network alignment, in general, seeks to discover the hidden underlying correspondence
between nodes across two (or more) networks when given their network structure. However …

Network alignment is the task of identifying topologically and semantically similar nodes
across (two) different networks. It plays an important role in various applications ranging …

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

Open source software (OSS) allows developers to study, change, and improve the code free
of charge. There are several high-quality software projects which deliver stable and well …