Community detection, also known as graph clustering, has been extensively studied in the
literature. The goal of community detection is to partition vertices in a complex graph into …

Spectral clustering is a clustering method based on algebraic graph theory. It has aroused
extensive attention of academia in recent years, due to its solid theoretical foundation, as …

The construction of a meaningful graph plays a crucial role in the success of many graph-
based representations and algorithms for handling structured data, especially in the …

The representation of a knowledge graph (KG) in a latent space recently has attracted more
and more attention. To this end, some proposed models (eg, TransE) embed entities and …

Background Methods for the integrative analysis of multi-omics data are required to draw a
more complete and accurate picture of the dynamics of molecular systems. The complexity …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …

Maritime situational awareness (MSA) has long been a critical focus within the domain of
maritime traffic surveillance and management. The increasing complexities of ship traffic …

Relationships between entities in datasets are often of multiple nature, like geographical
distance, social relationships, or common interests among people in a social network, for …

This paper focuses on two fundamental tasks of graph analysis: community detection and
node representation learning, which capture the global and local structures of graphs …

In recent years there has been an increased interest in statistical analysis of data with
multiple types of relations among a set of entities. Such multi-relational data can be …