An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks
In the study of complex networks, a network is said to have community structure if it divides naturally into groups of nodes with dense connections within groups and only sparser connections between them [1]. Community structures are quite common in real networks. Social networks often include commu...
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| Published in | Knowledge-based systems Vol. 37; pp. 19 - 36 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.01.2013
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| ISSN | 0950-7051 1872-7409 |
| DOI | 10.1016/j.knosys.2012.05.021 |
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| Abstract | In the study of complex networks, a network is said to have community structure if it divides naturally into groups of nodes with dense connections within groups and only sparser connections between them [1]. Community structures are quite common in real networks. Social networks often include community groups based on common location, interests, occupation, etc. One of the most widely used methods for community detection is modularity maximization [2]. Modularity is a function that measures the quality of a particular division of a network into communities. But in [3], it is shown that communities that maximize the modularity are certainly groupings of smaller communities that need to be studied. In this work, we define a new function that qualifies a partition. We also present an algorithm that optimizes this function in order to find, within a reasonable time, the partition with the best measure of quality and which does not ignore small community. |
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| AbstractList | In the study of complex networks, a network is said to have community structure if it divides naturally into groups of nodes with dense connections within groups and only sparser connections between them [1]. Community structures are quite common in real networks. Social networks often include community groups based on common location, interests, occupation, etc. One of the most widely used methods for community detection is modularity maximization [2]. Modularity is a function that measures the quality of a particular division of a network into communities. But in [3], it is shown that communities that maximize the modularity are certainly groupings of smaller communities that need to be studied. In this work, we define a new function that qualifies a partition. We also present an algorithm that optimizes this function in order to find, within a reasonable time, the partition with the best measure of quality and which does not ignore small community. In the study of complex networks, a network is said to have community structure if it divides naturally into groups of nodes with dense connections within groups and only sparser connections between them. Community structures are quite common in real networks. Social networks often include community groups based on common location, interests, occupation, etc. One of the most widely used methods for community detection is modularity maximization. Modularity is a function that measures the quality of a particular division of a network into communities. But in, it is shown that communities that maximize the modularity are certainly groupings of smaller communities that need to be studied. In this work, we define a new function that qualifies a partition. We also present an algorithm that optimizes this function in order to find, within a reasonable time, the partition with the best measure of quality and which does not ignore small community. |
| Author | Zardi, H. Romdhane, L. Ben |
| Author_xml | – sequence: 1 givenname: H. surname: Zardi fullname: Zardi, H. email: zardidarine@yahoo.fr organization: Faculty of Sciences, University of Monastir, Tunisia – sequence: 2 givenname: L. Ben surname: Romdhane fullname: Romdhane, L. Ben organization: High School of Sciences and Technology of Hammam Sousse, University of Sousse, Tunisia |
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| Keywords | Community detection Social networks Graph theory Objective function Modularity maximization |
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Modularity and community structure in networks publication-title: Proceedings of the National Academy of Sciences of the United States of America doi: 10.1073/pnas.0601602103 – start-page: P10008 year: 2008 ident: 10.1016/j.knosys.2012.05.021_b0155 article-title: Fast unfolding of communities in large networks publication-title: Journal of Statistical Mechanics: Theory and Experiment doi: 10.1088/1742-5468/2008/10/P10008 |
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| Title | An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks |
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