Networks are a fundamental tool for understanding and modeling complex systems in
physics, biology, neuroscience, engineering, and social science. Many networks are known …

We introduce a new algorithm for modularity-based community detection in large networks.
The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

We offer a new metric for big data platforms, COST, or the Configuration that Outperforms a
Single Thread. The COST of a given platform for a given problem is the hardware …

As recently as the 1990s, studies showed that most people preferred getting information
from other people rather than from information retrieval systems. Of course, in that time …

Information retrieval (IR) has changed considerably in recent years with the expansion of the
World Wide Web and the advent of modern and inexpensive graphical user interfaces and …

GPS (for Graph Processing System) is a complete open-source system we developed for
scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large …

Studying web graphs is often difficult due to their large size. Recently, several proposals
have been published about various techniques that allow tostore a web graph in memory in …

Breadth‐First Search is an important kernel used by many graph‐processing applications. In
many of these emerging applications of BFS, such as analyzing social networks, the input …

We introduce and analyze stochastic optimization methods where the input to each update
is perturbed by bounded noise. We show that this framework forms the basis of a unified …