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Efficient Shortest Paths on Massive Social Graphs (Invited Paper)

Summary: Efficient Shortest Paths on Massive Social Graphs
(Invited Paper)
Xiaohan Zhao, Alessandra Sala, Haitao Zheng and Ben Y. Zhao
Department of Computer Science, U. C. Santa Barbara, Santa Barbara, USA
{xiaohanzhao, alessandra, htzheng, ravenben}@cs.ucsb.edu
Abstract--Analysis of large networks is a critical component
of many of today's application environments, including online
social networks, protein interactions in biological networks, and
Internet traffic analysis. The arrival of massive network graphs
with hundreds of millions of nodes, e.g. social graphs, presents
a unique challenge to graph analysis applications. Most of these
applications rely on computing distances between node pairs,
which for large graphs can take minutes to compute using
traditional algorithms such as breadth-first-search (BFS).
In this paper, we study ways to enable scalable graph process-
ing for today's massive networks. We explore the design space
of graph coordinate systems, a new approach that accurately
approximates node distances in constant time by embedding
graphs into coordinate spaces. We show that a hyperbolic
embedding produces relatively low distortion error, and propose


Source: Almeroth, Kevin C. - Department of Computer Science, University of California at Santa Barbara


Collections: Computer Technologies and Information Sciences