Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels
have murdered 150,000 people since 2006; upwards of 700,000 people per year are …

Listing all k-cliques in a graph is a fundamental graph mining problem that finds many
important applications in community detection and social network analysis. Unfortunately …

We present Chronos, a framework to build accelerators for applications with speculative
parallelism. These applications consist of atomic tasks, sometimes with order constraints …

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that
facilitates evaluating and constructing high-performance graph mining algorithms. First …

We develop the first parallel graph coloring heuristics with strong theoretical guarantees on
work and depth and coloring quality. The key idea is to design a relaxation of the vertex …

In this article, we show that many sequential randomized incremental algorithms are in fact
parallel. We consider algorithms for several problems, including Delaunay triangulation …

Graph algorithms are challenging to parallelize on manycore architectures due to complex
data dependencies and irregular memory access. We consider the well studied problem of …

Some recent papers showed that many sequential iterative algorithms can be directly
parallelized, by identifying the dependences between the input objects. This approach …