Designing effective algorithms for community detection is an important and challenging problem in large-scale graphs, studied extensively in the literature. Various solutions have been proposed, but many of them are centralized with expensive procedures
(requiring full knowledge of the input graph) and have
a large running time. In this paper, we present a distributed algorithm for community detection in the stochastic block model (also called planted partition model), a
widely-studied and canonical random graph model
for community detection and clustering. Our algorithm called CDRW(Community Detection by Random Walks) is based on random walks, and is localized and
lightweight, and easy to implement. A novel feature of
the algorithm is that it uses the concept of local mixing
time to identify the community around a given node.
We present a rigorous theoretical analysis that shows
that the algorithm can accurately identify the communities in the stochastic block model and characterize the model parameters where the algorithm works. We
also present experimental results that validate our
theoretical analysis. We also analyze the performance
of our distributed algorithm under the CONGEST
distributed model as well as the k-machine model, a
model for large-scale distributed computations, and
show that it can be efficiently implemented.
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Fast Stochastic Block Partitioning via Sampling
Community detection in graphs, also known as graph partitioning, is a well-studied NP-hard problem. Various heuristic approaches have been adopted to tackle this problem in polynomial time. One such approach, as outlined in the IEEE HPEC Graph Challenge, is Bayesian statistics-based stochastic block partitioning. This method delivers high-quality partitions in sub-quadratic runtime, but it fails to scale to very large graphs. In this paper, we present sampling as an avenue for speeding up the algorithm on large graphs. We first show that existing sampling techniques can preserve a graph’s community structure. We then show that sampling for stochastic block partitioning can be used to produce a speedup of between 2.18× and 7.26× for graph sizes between 5, 000 and 50, 000 vertices without a significant loss in the accuracy of community detection.
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- Award ID(s):
- 1822080
- NSF-PAR ID:
- 10188574
- Date Published:
- Journal Name:
- IEEE High Performance Extreme Computing Conference
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HPEC.2019.8916542
