While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

The maximum clique problem (MCP) is to determine in a graph a clique (ie, a complete
subgraph) of maximum cardinality. The MCP is notable for its capability of modeling other …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …

How can web services that depend on user generated content discern fraudulent input by
spammers from legitimate input? In this paper we focus on the social network Facebook and …

The k-truss is a type of cohesive subgraphs proposed recently for the study of networks.
While the problem of computing most cohesive subgraphs is NP-hard, there exists a …

Multilayer networks, in particular multilayer social networks, where users belong to and
interact on different networks at the same time, are an active research area in social network …

Graph mining is an important research area within the domain of data mining. The field of
study concentrates on the identification of frequent subgraphs within graph data sets. The …

Clustering a graph, ie, assigning its nodes to groups, is an important operation whose best
known application is the discovery of communities in social networks. Graph clustering and …

Given a large online network of online auction users and their histories of transactions, how
can we spot anomalies and auction fraud? This paper describes the design and …

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 …