Graphs are powerful data representations favored in many computational domains. Modern
GPUs have recently shown promising results in accelerating computationally challenging …

Graphs are widely used to model real world objects and their relationships, and large graph
datasets are common in many application domains. To understand the underlying …

Betweenness is a centrality measure based on shortest paths, widely used in complex
network analysis. It is computationally-expensive to exactly determine betweenness; …

In most real-world networks, the nodes/vertices tend to be organized into tightly-knit modules
known as communities or clusters, such that nodes within a community are more likely to be" …

The advent of social network sites in the last years seems to be a trend that will likely
continue. What naive technology users may not realize is that the information they provide …

Many important problems in computational sciences, social network analysis, security, and
business analytics, are data-intensive and lend themselves to graph-theoretical analyses. In …

With the prevalence of graph data in real-world applications (eg, social networks, mobile
phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph …

We present topic-regression multi-modal Latent Dirich-let Allocation (tr-mmLDA), a novel
statistical topic model for the task of image and video annotation. At the heart of our new …

Many applications require to learn, mine, analyze and visualize large-scale graphs. These
graphs are often too large to be addressed efficiently using conventional graph processing …

The studies of k-truss based community search demonstrated that it can find high-quality
personalized com-munities with good properties such as high connectivity and bounded …