Hypergraphs naturally represent group interactions, which are omnipresent in many
domains: collaborations of researchers, co-purchases of items, joint interactions of proteins …

In many modern applications, the generated data is a dynamic network. These networks are
graphs that change over time by a sequence of update operations (node addition, node …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

We present MaNIACS, a sampling-based randomized algorithm for computing high-quality
approximations of the collection of the subgraph patterns that are frequent in a single, large …

Cohesive subgraph mining is a fundamental problem in bipartite graph analysis. In reality,
relationships between two types of entities often occur at some specific timestamps, which …

Hypergraphs naturally represent group interactions, which are omnipresent in many
domains: collaborations of researchers, co-purchases of items, and joint interactions of …

Frequent pattern mining is a key area of study that gives insights into the structure and
dynamics of evolving networks, such as social or road networks. However, not only does a …

Graph data management systems are designed for managing highly interconnected data.
However, most of the existing work on the topic does not take into account the temporal …

With the popularity of graph applications, frequent pattern mining (FPM) has been playing a
significant role in many domains, such as social networks and bioinformatics. However, due …