Community detection in networks: A multidisciplinary review

Javed, Muhammad Aqib; Younis, Muhammad Shahzad; Latif, Siddique; Qadir, Junaid; Baig, Adeel

doi:10.1016/j.jnca.2018.02.011

Cited by 392 publications

(170 citation statements)

References 162 publications

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“…Obviously, this definition of communities depends on the particular quality function Q used. A large number of quality functions have been proposed in [47]. The QACR protocol uses the Girvan-Newman modularity maximization [46], the most popular quality function.…”

Section: ( )mentioning

confidence: 99%

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

Hosen

Singh

Sharma

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Improving quality of service (QoS) of low power and lossy networks (LLNs) in internet of things (IoT) is a major challenge. Cluster-based routing technique is an effective approach to achieve this goal. This paper proposes a QoS-aware clustering-based routing (QACR) mechanism for LLNs in Fogenabled IoT which provides a clustering, a cluster head (CH) election, and a routing path selection technique. The clustering adopts the community detection algorithm that partitions the network into clusters with available nodes' connectivity. The CH election and relay node selection both are weighted by the rank of the nodes which take node's energy, received signal strength, link quality, and number of cluster members into consideration as the ranking metrics. The number of CHs in a cluster is adaptive and varied according to a cluster state to balance the energy consumption of nodes. Besides, the protocol uses the CH role handover technique during CH election that decreases the control messages for the periodic election and cluster formation in detail. An evaluation of the QACR has performed through simulations for various scenarios. The obtained results show that the QACR improves the QoS in terms of packet delivery ratio, latency, and network lifetime compared to the existing protocols.

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Section: ( )mentioning

confidence: 99%

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

Hosen

Singh

Sharma

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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“…While a lot of research has been reported on graph clustering (for a comprehensive review see the articles of Fortunato [7] and Malliaros & Vazirgiannis [8] for directed graphs, as well as the two recent reviews by Javed et al [9] and Fortunato & Hric [10]) not much research work has been conducted for the detection of central communities of graphs and especially of graphs corresponding to egocentric networks. This trend can be attributed to three reasons: (1) the detection of central community of a graph is considered a special case of graph clustering, (2) the central community of a graph is assumed to be well approximated by its degeneracy core and the influential algorithm of Batagelj & Zaveršnik [11] for k-core detection was implemented in the majority of network analysis software including Python's networkx library, and (3) the usefulness of central community as an approximation of larger networks was not sufficiently pointed up.…”

Section: Background and Related Workmentioning

confidence: 99%

The Central Community of Twitter ego-Networks as a Means for Fake Influencer Detection

Tsapatsoulis

Anastasopoulou

Ntalianis

2019

2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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The central community of social networks, usually represented through the highest degree k-core of the corresponding graph, is proposed here as a compact representation of large social networks. We show that the central community of egocentric social media networks, such as the ego networks on Twitter and Instagram, tell us much more about the actual influence of the ego than the whole egocentric network itself. We also propose a novel genetic algorithm for the identification of central community of egocentric social networks and we examine the importance of the proper initialisation of this algorithm. The actual Twitter ego networks we used in our experiments along with the corresponding Python code are made publicly available for anyone who wishes to use them.

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“…Users can independently pay attention to the home page of interest and express their views. The instantaneity and openness of the information platform promotes the gathering of users with common values and interests, and user communities will form Users with high degrees of relevance in online social networks usually aggregate to constitute a group [18]. Microblog is a real-time medium, and user interests mainly stem from users interested in friends and acquaintances.…”

Section: Introductionmentioning

confidence: 99%

Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach

Zhou

Tian

2019

Future Internet

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The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets.

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Community detection in networks: A multidisciplinary review

Cited by 392 publications

References 162 publications

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

The Central Community of Twitter ego-Networks as a Means for Fake Influencer Detection

Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach

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