36,384 research outputs found
Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm
Many systems can be described using graphs, or networks. Detecting
communities in these networks can provide information about the underlying
structure and functioning of the original systems. Yet this detection is a
complex task and a large amount of work was dedicated to it in the past decade.
One important feature is that communities can be found at several scales, or
levels of resolution, indicating several levels of organisations. Therefore
solutions to the community structure may not be unique. Also networks tend to
be large and hence require efficient processing. In this work, we present a new
algorithm for the fast detection of communities across scales using a local
criterion. We exploit the local aspect of the criterion to enable parallel
computation and improve the algorithm's efficiency further. The algorithm is
tested against large generated multi-scale networks and experiments demonstrate
its efficiency and accuracy.Comment: arXiv admin note: text overlap with arXiv:1204.100
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
Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks
Nowadays, identification and detection community structures in complex
networks is an important factor in extracting useful information from networks.
Label propagation algorithm with near linear-time complexity is one of the most
popular methods for detecting community structures, yet its uncertainty and
randomness is a defective factor. Merging LPA with other community detection
metrics would improve its accuracy and reduce instability of LPA. Considering
this point, in this paper we tried to use edge betweenness centrality to
improve LPA performance. On the other hand, calculating edge betweenness
centrality is expensive, so as an alternative metric, we try to use local edge
betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge
Betweenness). Experimental results on both real-world and benchmark networks
show that LPA-LEB possesses higher accuracy and stability than LPA when
detecting community structures in networks.Comment: 6 page
Fast community structure local uncovering by independent vertex-centred process
This paper addresses the task of community detection and proposes a local
approach based on a distributed list building, where each vertex broadcasts
basic information that only depends on its degree and that of its neighbours. A
decentralised external process then unveils the community structure. The
relevance of the proposed method is experimentally shown on both artificial and
real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Minin
Overlapping Community Detection Optimization and Nash Equilibrium
Community detection using both graphs and social networks is the focus of
many algorithms. Recent methods aimed at optimizing the so-called modularity
function proceed by maximizing relations within communities while minimizing
inter-community relations.
However, given the NP-completeness of the problem, these algorithms are
heuristics that do not guarantee an optimum. In this paper, we introduce a new
algorithm along with a function that takes an approximate solution and modifies
it in order to reach an optimum. This reassignment function is considered a
'potential function' and becomes a necessary condition to asserting that the
computed optimum is indeed a Nash Equilibrium. We also use this function to
simultaneously show partitioning and overlapping communities, two detection and
visualization modes of great value in revealing interesting features of a
social network. Our approach is successfully illustrated through several
experiments on either real unipartite, multipartite or directed graphs of
medium and large-sized datasets.Comment: Submitted to KD
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