Overlapping Modularity at the Critical Point of k-Clique Percolation

  title={Overlapping Modularity at the Critical Point of k-Clique Percolation},
  author={B{\'a}lint J. T{\'o}th and Tam{\'a}s Vicsek and Gergely Palla},
  journal={Journal of Statistical Physics},
One of the most remarkable social phenomena is the formation of communities in social networks corresponding to families, friendship circles, work teams, etc. Since people usually belong to several different communities at the same time, the induced overlaps result in an extremely complicated web of the communities themselves. Thus, uncovering the intricate community structure of social networks is a non-trivial task with great potential for practical applications, gaining a notable interest in… 

Extended Clique percolation method to detect overlapping community structure

  • S. MaityS. K. Rath
  • Computer Science
    2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
  • 2014
A novel approach has been introduced to extend the clique percolation method so that each and every connected node will be part of at least one community.

Detection of Overlapping Communities in Social Network

A novel approach has been introduced which overcomes the shortfalls of clique percolation method, an overlapping community detection algorithm mostly used in this area, which efficiently detect overlapping communities.

A computational geometric approach for overlapping community (cover) detection in social network

  • V. SumithraSubu Surendran
  • Computer Science
    2015 International Conference on Computing and Network Communications (CoCoNet)
  • 2015
The proposed work performs cover detection by following a computational geometry and fuzzy approach and a new height balanced tree called Cluster Tree (C-Tree) is introduced here for clustering.

Analysis of community-detection methods based on Potts spin model in complex networks

A critical analysis of the multiscale methods based on Potts spin model for community detection are described and compared in the analysis of community structures of several networks, showing a kind of limitation that the methods may suffer from when the community size difference is very broad.

Uncovering Research Topics of Academic Communities of Scientific Collaboration Network

A nonjoint approach, consisting of three simple steps to detect overlapping academic communities in SCN with the clique percolation method, to discover underlying topics and research interests of each researcher with author-topic (AT) model, and to label research topics of each community with top N most frequent collaborative topics between members belonging to the community.

Square percolation and the threshold for quadratic divergence in random right‐angled Coxeter groups

Given a graph Γ , its auxiliary square‐graph □(Γ) is the graph whose vertices are the non‐edges of Γ and whose edges are the pairs of non‐edges which induce a square (i.e., a 4‐cycle) in Γ . We

Uncovering research trends and topics of communities in machine learning

An integrated approach combining the author-topic (AT) model with communities using through the directed affiliations (CoDA) method is explored to identify the research interest of the communities in a scientific collaboration network.

Resilience Analysis of Australian Electricity and Gas Transmission Networks

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or

A Complete Bibliography of the Journal of Statistical Physics: 2000{2009

(2 + 1) [XTpXpH12, CTH11]. + [Zuc11b]. 0 [Fed17]. 1 [BELP15, CAS11, Cor16, Fed17, GDL10, GBL16, Hau16, JV19, KT12, KM19c, Li19, MN14b, Nak17, Pal11, Pan14, RT14, RBS16b, RY12, SS18c, Sug10, dOP18]. 1



The Critical Point of k-Clique Percolation in the Erdős–Rényi Graph

Motivated by the success of a k-clique percolation method for the identification of overlapping communities in large real networks, here we study the k-clique percolation problem in the Erdős–Rényi

Quantifying social group evolution

The focus is on networks capturing the collaboration between scientists and the calls between mobile phone users, and it is found that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability.

Community structure in social and biological networks

  • M. GirvanM. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.

Quantifying and identifying the overlapping community structure in networks

A metric is proposed that assumes that a maximal clique only belongs to one community, and it is proved that the optimization of the metric on the original network is equivalent to the optimizing of Newman’s modularity on the maximalClique network.

Community detection in graphs

Extending the definition of modularity to directed graphs with overlapping communities

This paper starts from the definition of a modularity function, given by Newman to evaluate the goodness of network community decompositions, and extends it to the more general case of directed graphs with overlapping community structures.

Fast unfolding of community hierarchies in large networks

This work decomposes the networks into communities of strongly connected nodes, with the nodes belonging to different communities only sparsely connected, and proposes algorithms to find reasonably “good” solutions of the problem in a reasonably ”fast” way.

Detecting the overlapping and hierarchical community structure in complex networks

The first algorithm that finds both overlapping communities and the hierarchical structure is presented, based on the local optimization of a fitness function, enabling different hierarchical levels of organization to be investigated.

Fuzzy communities and the concept of bridgeness in complex networks.

An algorithm for determining the optimal membership degrees with respect to a given goal function is created, and a measure is introduced that is able to identify outlier vertices that do not belong to any of the communities, bridges that have significant membership in more than one single community, and regular Vertices that fundamentally restrict their interactions within their own community.

Resolution limit in community detection

It is found that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined.