As one of the most fundamental problems in graph data mining, the densest subgraph
discovery (DSD) problem has found a broad spectrum of real applications, such as social …

Multilayer networks are a powerful paradigm to model complex systems, where multiple
relations occur between the same entities. Despite the keen interest in a variety of tasks …

Given a stream of edge additions and deletions, how can we estimate the count of triangles
in it? If we can store only a subset of the edges, how can we obtain unbiased estimates with …

With the prevalence of graphs for modeling complex relationships among objects, the topic
of graph mining has attracted a great deal of attention from both academic and industrial …

Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has
recently received significant attention. In this paper, we address the problem of identifying a …

A central problem in graph mining is finding dense subgraphs, with several applications in
different fields, a notable example being identifying communities. While a lot of effort has …

Computing the densest subgraph is a primitive graph operation with critical applications in
detecting communities, events, and anomalies in biological, social, Web, and financial …

With the rapid development of information technology, huge volumes of graph data are
accumulated. Real graphs are usually sparsely connected from a global point of view, but …

Temporal networks have been successfully applied to analyse dynamics of networks. In this
paper we focus on an approach recently introduced to identify dense subgraphs in a …

Community detection is one of the fundamental tasks in graph mining, which has many real-
world applications in diverse domains. In this study, we propose an optimization model for …