2,097 research outputs found
Gunrock: A High-Performance Graph Processing Library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs have been two
significant challenges for developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We evaluate Gunrock on five key graph
primitives and show that Gunrock has on average at least an order of magnitude
speedup over Boost and PowerGraph, comparable performance to the fastest GPU
hardwired primitives, and better performance than any other GPU high-level
graph library.Comment: 14 pages, accepted by PPoPP'16 (removed the text repetition in the
previous version v5
A Divide-and-Conquer Algorithm for Betweenness Centrality
The problem of efficiently computing the betweenness centrality of nodes has
been researched extensively. To date, the best known exact and centralized
algorithm for this task is an algorithm proposed in 2001 by Brandes. The
contribution of our paper is Brandes++, an algorithm for exact efficient
computation of betweenness centrality. The crux of our algorithm is that we
create a sketch of the graph, that we call the skeleton, by replacing subgraphs
with simpler graph structures. Depending on the underlying graph structure,
using this skeleton and by keeping appropriate summaries Brandes++ we can
achieve significantly low running times in our computations. Extensive
experimental evaluation on real life datasets demonstrate the efficacy of our
algorithm for different types of graphs. We release our code for benefit of the
research community.Comment: Shorter version of this paper appeared in Siam Data Mining 201
Numerical Investigation of Metrics for Epidemic Processes on Graphs
This study develops the epidemic hitting time (EHT) metric on graphs
measuring the expected time an epidemic starting at node in a fully
susceptible network takes to propagate and reach node . An associated EHT
centrality measure is then compared to degree, betweenness, spectral, and
effective resistance centrality measures through exhaustive numerical
simulations on several real-world network data-sets. We find two surprising
observations: first, EHT centrality is highly correlated with effective
resistance centrality; second, the EHT centrality measure is much more
delocalized compared to degree and spectral centrality, highlighting the role
of peripheral nodes in epidemic spreading on graphs.Comment: 6 pages, 1 figure, 3 tables, In Proceedings of 2015 Asilomar
Conference on Signals, Systems, and Computer
Message passing optimization of Harmonic Influence Centrality
This paper proposes a new measure of node centrality in social networks, the
Harmonic Influence Centrality, which emerges naturally in the study of social
influence over networks. Using an intuitive analogy between social and
electrical networks, we introduce a distributed message passing algorithm to
compute the Harmonic Influence Centrality of each node. Although its design is
based on theoretical results which assume the network to have no cycle, the
algorithm can also be successfully applied on general graphs.Comment: 11 pages; 10 figures; to appear as a journal publicatio
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