Mining dense subgraphs such as cliques or quasi-cliques is an important graph mining
problem and closely related to the notion of graph clustering. In various applications, graphs …

Given a co-authorship collaboration network, how well can we cluster the participating
authors into communities? If we also consider their citation network, based on the same …

High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in
transaction databases, which considers both quantity and profit of items. However, most …

Graph pattern matching is finding all matches in a data graph for a given pattern graph and
is often defined in terms of subgraph isomorphism, an np-complete problem. To lower its …

Conformance checking is a key process mining task to confront the normative behavior
imposed by a process model with the actual behavior recorded in a log. While this problem …

Discovering frequent graph patterns in a graph database offers valuable information in a
variety of applications. However, if the graph dataset contains sensitive data of individuals …

Due to the inaccuracy, incompleteness and noise in data from real applications, uncertainty
is a natural feature of real-world networks. In such networks, each edge is associated with a …

We consider in this paper the similarity search problem that retrieves relevant graphs from a
graph database under the well-known graph edit distance (GED) constraint. Formally, given …

Recent years have seen an explosion in graph data from a variety of scientific, social and
technological fields. From these fields, emotion recognition is an interesting research area …

Today's applications deal with multiple types of information: graph data to represent the
relations between objects and attribute data to characterize single objects. Analyzing both …