8,698 research outputs found
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
Detecting Communities in Networks by Merging Cliques
Many algorithms have been proposed for detecting disjoint communities
(relatively densely connected subgraphs) in networks. One popular technique is
to optimize modularity, a measure of the quality of a partition in terms of the
number of intracommunity and intercommunity edges. Greedy approximate
algorithms for maximizing modularity can be very fast and effective. We propose
a new algorithm that starts by detecting disjoint cliques and then merges these
to optimize modularity. We show that this performs better than other similar
algorithms in terms of both modularity and execution speed.Comment: 5 pages, 7 figure
Fast Detection of Community Structures using Graph Traversal in Social Networks
Finding community structures in social networks is considered to be a
challenging task as many of the proposed algorithms are computationally
expensive and does not scale well for large graphs. Most of the community
detection algorithms proposed till date are unsuitable for applications that
would require detection of communities in real-time, especially for massive
networks. The Louvain method, which uses modularity maximization to detect
clusters, is usually considered to be one of the fastest community detection
algorithms even without any provable bound on its running time. We propose a
novel graph traversal-based community detection framework, which not only runs
faster than the Louvain method but also generates clusters of better quality
for most of the benchmark datasets. We show that our algorithms run in O(|V | +
|E|) time to create an initial cover before using modularity maximization to
get the final cover.
Keywords - community detection; Influenced Neighbor Score; brokers; community
nodes; communitiesComment: 29 pages, 9 tables, and 13 figures. Accepted in "Knowledge and
Information Systems", 201
Node-Centric Detection of Overlapping Communities in Social Networks
We present NECTAR, a community detection algorithm that generalizes Louvain
method's local search heuristic for overlapping community structures. NECTAR
chooses dynamically which objective function to optimize based on the network
on which it is invoked. Our experimental evaluation on both synthetic benchmark
graphs and real-world networks, based on ground-truth communities, shows that
NECTAR provides excellent results as compared with state of the art community
detection algorithms
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