Biological networks such as protein interaction networks, gene regulation networks, and
metabolic pathways are examples of complex networks that are large graphs with small …

We study the problem of implementing graph algorithms efficiently on Pregel-like systems,
which can be surprisingly challenging. Standard graph algorithms in this setting can incur …

We describe an approach to parallel graph partitioning that scales to hundreds of
processors and produces a high solution quality. For example, for many instances from …

Combinatorial techniques have become essential tools across the landscape of
computational science, and some of the combinatorial ideas undergirding these tools are …

Tackling the current volume of graph-structured data requires parallel tools. We extend our
work on analyzing such massive graph data with the first massively parallel algorithm for …

We describe a half-approximation algorithm, b-Suitor, for computing a b-Matching of
maximum weight in a graph with weights on the edges. b-Matching is a generalization of the …

Tasks in the real world are complex and often require multiple robots to collaborate to be
serviced. In many cases, a task might require different sensory inputs and actuation outputs …

The volume of existing graph-structured data requires improved parallel tools and
algorithms. Finding communities, smaller sub graphs densely connected within the sub …

Matching is an important combinatorial problem with a number of applications in areas such
as community detection, sparse linear algebra, and network alignment. Since computing …

In this paper, we study the problem of allocating multiple heterogeneous robots to tasks. Due
to the limited capabilities of the robots, a task might need more than one robot to complete it …