91,645 research outputs found
Defining and Evaluating Network Communities based on Ground-truth
Nodes in real-world networks organize into densely linked communities where
edges appear with high concentration among the members of the community.
Identifying such communities of nodes has proven to be a challenging task
mainly due to a plethora of definitions of a community, intractability of
algorithms, issues with evaluation and the lack of a reliable gold-standard
ground-truth.
In this paper we study a set of 230 large real-world social, collaboration
and information networks where nodes explicitly state their group memberships.
For example, in social networks nodes explicitly join various interest based
social groups. We use such groups to define a reliable and robust notion of
ground-truth communities. We then propose a methodology which allows us to
compare and quantitatively evaluate how different structural definitions of
network communities correspond to ground-truth communities. We choose 13
commonly used structural definitions of network communities and examine their
sensitivity, robustness and performance in identifying the ground-truth. We
show that the 13 structural definitions are heavily correlated and naturally
group into four classes. We find that two of these definitions, Conductance and
Triad-participation-ratio, consistently give the best performance in
identifying ground-truth communities. We also investigate a task of detecting
communities given a single seed node. We extend the local spectral clustering
algorithm into a heuristic parameter-free community detection method that
easily scales to networks with more than hundred million nodes. The proposed
method achieves 30% relative improvement over current local clustering methods.Comment: Proceedings of 2012 IEEE International Conference on Data Mining
(ICDM), 201
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
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
On the Permanence of Vertices in Network Communities
Despite the prevalence of community detection algorithms, relatively less
work has been done on understanding whether a network is indeed modular and how
resilient the community structure is under perturbations. To address this
issue, we propose a new vertex-based metric called "permanence", that can
quantitatively give an estimate of the community-like structure of the network.
The central idea of permanence is based on the observation that the strength
of membership of a vertex to a community depends upon the following two
factors: (i) the distribution of external connectivity of the vertex to
individual communities and not the total external connectivity, and (ii) the
strength of its internal connectivity and not just the total internal edges.
In this paper, we demonstrate that compared to other metrics, permanence
provides (i) a more accurate estimate of a derived community structure to the
ground-truth community and (ii) is more sensitive to perturbations in the
network. As a by-product of this study, we have also developed a community
detection algorithm based on maximizing permanence. For a modular network
structure, the results of our algorithm match well with ground-truth
communities.Comment: 10 pages, 5 figures, 8 tables, Accepted in 20th ACM SIGKDD Conference
on Knowledge Discovery and Data Minin
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