15,661 research outputs found
A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection
Peer reviewedPostprin
Link communities reveal multiscale complexity in networks
Networks have become a key approach to understanding systems of interacting
objects, unifying the study of diverse phenomena including biological organisms
and human society. One crucial step when studying the structure and dynamics of
networks is to identify communities: groups of related nodes that correspond to
functional subunits such as protein complexes or social spheres. Communities in
networks often overlap such that nodes simultaneously belong to several groups.
Meanwhile, many networks are known to possess hierarchical organization, where
communities are recursively grouped into a hierarchical structure. However, the
fact that many real networks have communities with pervasive overlap, where
each and every node belongs to more than one group, has the consequence that a
global hierarchy of nodes cannot capture the relationships between overlapping
groups. Here we reinvent communities as groups of links rather than nodes and
show that this unorthodox approach successfully reconciles the antagonistic
organizing principles of overlapping communities and hierarchy. In contrast to
the existing literature, which has entirely focused on grouping nodes, link
communities naturally incorporate overlap while revealing hierarchical
organization. We find relevant link communities in many networks, including
major biological networks such as protein-protein interaction and metabolic
networks, and show that a large social network contains hierarchically
organized community structures spanning inner-city to regional scales while
maintaining pervasive overlap. Our results imply that link communities are
fundamental building blocks that reveal overlap and hierarchical organization
in networks to be two aspects of the same phenomenon.Comment: Main text and supplementary informatio
Communities as Well Separated Subgraphs With Cohesive Cores: Identification of Core-Periphery Structures in Link Communities
Communities in networks are commonly considered as highly cohesive subgraphs
which are well separated from the rest of the network. However, cohesion and
separation often cannot be maximized at the same time, which is why a
compromise is sought by some methods. When a compromise is not suitable for the
problem to be solved it might be advantageous to separate the two criteria. In
this paper, we explore such an approach by defining communities as well
separated subgraphs which can have one or more cohesive cores surrounded by
peripheries. We apply this idea to link communities and present an algorithm
for constructing hierarchical core-periphery structures in link communities and
first test results.Comment: 12 pages, 2 figures, submitted version of a paper accepted for the
7th International Conference on Complex Networks and Their Applications,
December 11-13, 2018, Cambridge, UK; revised version at
http://141.20.126.227/~qm/papers
Evidential Communities for Complex Networks
Community detection is of great importance for understand-ing graph structure
in social networks. The communities in real-world networks are often
overlapped, i.e. some nodes may be a member of multiple clusters. How to
uncover the overlapping communities/clusters in a complex network is a general
problem in data mining of network data sets. In this paper, a novel algorithm
to identify overlapping communi-ties in complex networks by a combination of an
evidential modularity function, a spectral mapping method and evidential
c-means clustering is devised. Experimental results indicate that this
detection approach can take advantage of the theory of belief functions, and
preforms good both at detecting community structure and determining the
appropri-ate number of clusters. Moreover, the credal partition obtained by the
proposed method could give us a deeper insight into the graph structure
DEMON: a Local-First Discovery Method for Overlapping Communities
Community discovery in complex networks is an interesting problem with a
number of applications, especially in the knowledge extraction task in social
and information networks. However, many large networks often lack a particular
community organization at a global level. In these cases, traditional graph
partitioning algorithms fail to let the latent knowledge embedded in modular
structure emerge, because they impose a top-down global view of a network. We
propose here a simple local-first approach to community discovery, able to
unveil the modular organization of real complex networks. This is achieved by
democratically letting each node vote for the communities it sees surrounding
it in its limited view of the global system, i.e. its ego neighborhood, using a
label propagation algorithm; finally, the local communities are merged into a
global collection. We tested this intuition against the state-of-the-art
overlapping and non-overlapping community discovery methods, and found that our
new method clearly outperforms the others in the quality of the obtained
communities, evaluated by using the extracted communities to predict the
metadata about the nodes of several real world networks. We also show how our
method is deterministic, fully incremental, and has a limited time complexity,
so that it can be used on web-scale real networks.Comment: 9 pages; Proceedings of the 18th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 201
Community detection algorithms: a comparative analysis
Uncovering the community structure exhibited by real networks is a crucial
step towards an understanding of complex systems that goes beyond the local
organization of their constituents. Many algorithms have been proposed so far,
but none of them has been subjected to strict tests to evaluate their
performance. Most of the sporadic tests performed so far involved small
networks with known community structure and/or artificial graphs with a
simplified structure, which is very uncommon in real systems. Here we test
several methods against a recently introduced class of benchmark graphs, with
heterogeneous distributions of degree and community size. The methods are also
tested against the benchmark by Girvan and Newman and on random graphs. As a
result of our analysis, three recent algorithms introduced by Rosvall and
Bergstrom, Blondel et al. and Ronhovde and Nussinov, respectively, have an
excellent performance, with the additional advantage of low computational
complexity, which enables one to analyze large systems.Comment: 12 pages, 8 figures. The software to compute the values of our
general normalized mutual information is available at
http://santo.fortunato.googlepages.com/inthepress
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