Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

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 …

Supplementary material for “Fast communication detetion by SCORE”. Owing to space
constraints, the technical proofs are relegated a supplementary document. The …

How do we spot interesting events from e-mail or transportation logs? How can we detect
port scan or denial of service attacks from IP-IP communication data? In general, given a …

Given a graph of the money transfers between accounts of a bank, how can we detect
money laundering? Money laundering refers to criminals using the bank's services to move …

Benford's law describes the distribution of the first digit of numbers appearing in a wide
variety of numerical data, including tax records, and election outcomes, and has been used …

Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other
networking sites, the social media shared by users and the associated metadata are …

Detecting anomalies on graph data has two types of methods. One is pattern mining that
discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks …

Given a directed graph of millions of nodes, how can we automatically spot anomalous,
suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns …

Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding
of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS …