We develop new methods based on graph motifs for graph clustering, allowing more
efficient detection of communities within networks. We focus on triangles within graphs, but …

Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

The theory of structural holes suggests that individuals would benefit from filling the"
holes"(called as structural hole spanners) between people or groups that are otherwise …

Densest subgraph computation has emerged as an important primitive in a wide range of
data analysis tasks such as community and event detection. Social media such as Facebook …

In the age of Big Data, efficient algorithms are now in higher demand more than ever before.
While Big Data takes us into the asymptotic world envisioned by our pioneers, it also …

We give the first fully dynamic algorithm which maintains a (1− є)-approximate densest
subgraph in worst-case time poly (log n, є− 1) per update. Dense subgraph discovery is an …

Social networks are rich in various kinds of contents such as text and multimedia. The ability
to apply text mining algorithms effectively in the context of text data is critical for a wide …

In this paper we study the densest subgraph problem, which plays a key role in many graph
mining applications. The goal of the problem is to find a subset of nodes that induces a …

In many social networks, there exist two types of users that exhibit different influence and
different behavior. For instance, statistics have shown that less than 1% of the Twitter users …

In this paper, we propose and study a novel cohesive subgraph model, named (k, p)-core,
which is a maximal subgraph where each vertex has at least k neighbours and at least p …