A Multi-Population Cultural Algorithm for Community Detection in Social Networks

Zadeh, Pooya Moradian; Kobti, Ziad

doi:10.1016/j.procs.2015.05.105

Cited by 51 publications

(15 citation statements)

References 10 publications

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“…Despite various techniques have been utilized recently for discovering the densely connected clusters in networks, this crucial problem in various disciplines is still considered an open and a hard problem and has not been tackled satisfactorily since the networks have usually complex nature [14], [20]. More particularly, in huge networks, discovering successfully their module structure is still considered a great data mining problem [21].…”

Section: Optimization and Community Detection In Networkmentioning

confidence: 99%

On The Use of Optimization for Mining Clusters in Networks

Abd-Alsabour¹

2020

International Journal of Engineering Research and Technology

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In the real life, networks can abstract various complex systems. Organizing networks' vertices into coherent subgroups (clusters, groups, or communities) is considered one of the essential rules of complicated networks. A crucial feature of complex systems is having a community structure. An efficient methodology for tackling this vital feature is the community detection. The discovering of communities in these real-world complex networks is vital, as it helps gain strategic insights leading to crucial decisions, and realizes and discovers the dynamics of these systems in the real world. Detecting communities in networks has been recently realized as one of the major research areas in various domains such as science, physics, biology, marketing, engineering, ecology, political sciences, and economics. Meanwhile, the significance of optimization and subsequently the importance of the optimization approaches have been recently emphasized. This is because almost all hard applications and real-world ones deal with maximizing or minimizing some quantity to improve some outcome. Although many hard problems can be handled by numerous optimization approaches, there exist many factors for which applying these approaches for dealing with the community detection problem needs much research. This work researches this important research point with the use of community detection optimization approaches each represents a crucial class of optimization algorithms. In addition, the essentials of discovering clusters in networks are detailed.

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Section: Optimization and Community Detection In Networkmentioning

confidence: 99%

On The Use of Optimization for Mining Clusters in Networks

Abd-Alsabour¹

2020

International Journal of Engineering Research and Technology

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“…In recent years, some attempts tried to show that community structures are one of the significant characteristics in the most complex networks such as social networks due to numerous trends of human being to forming groups or communities. Due to the significant applications of community detection, several community detection approaches have been presented in literature which can be classified into six categories: spectral and clustering methods [20], [21], [15], [22], hierarchical algorithms [23], modularity-based methods [24], [25], evolutionary modelbased methods [26], [27], local community detection methods, and feature-based assisted methods [11]. Along with that total sixteen articles (published in 2015 to 2017) presented in this survey are summarized in Table 1 that contains eight columns.…”

Section: Community Detection Over Snsmentioning

confidence: 99%

Review of Community Detection over Social Media:Graph Prospective

Jain¹,

Singh²

2019

ijacsa

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Community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. Community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

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“…of generations for unimproved fittest chromosome fraction of mined hubs, no. of communities Tasgin et al [55] Modularity optimization Modularity, population size, number of chromosomes Zadeh [56] Multi population cultural algorithm BS-average, BSN [57] Iterative label propagation SLPA [58], WLPA [59], COPRA [60], Label Rank [61], BMPLA [62] Nodes,label [58], Labels,threshold [59], Label,similarity [60], nodes [ [65] CPM Nodes, threshold weight Lancichinetti et al [66] Works on the principle of the Fitness function Fitness function Du et al (Com Tector) [67] Kernels based clustering Set of all kernels Shen et al(EAGLE) [68] Agglomerative hierarchical clustering The similarity between the two communities Evans et al [69] Line graph, clique graph Edges,Partition Lee et al [70] Cliques based expansion Fitness funciton Gregory et al (CONGO [71], Peacock algorithm [72] Split between Local betweenness, short paths [71], the ratio of max, edge betweenness, and max,splitbetweeness [72]. [75] Clusters of overlapping vertices Internal edge intensity, external edge intensity,internal edge probability,edge ratio,intensity ratio Chen et al [76] Game theory Number of communities gain function, loss function Alvaro et al [77] Game theory based Number of snapshots, with V vertices and E edges Shi et al [78] Objective function: partition function Size of population,runninggenereation fraction of crossover,fraction of mutation Xing et al (OCDLCE) [79] Community detection Nodes,edges,neighbours of node Bhat et al ( OCMier) [80] Density-based Threshold Some traditional methods are easy but often need to decide the number or the size of clusters, other traditional methods are usually slow or need to know where to cut the dendrogram tree; Overlapping community detection methods can detect overlapping community but some methods are suitable for networks with many full connected subgraph or great uncertainty.…”

Section: Modularity Optimization Using Simulated Annealingmentioning

confidence: 99%

Community Detection Algorithms for Big Data using Graph Theory

2019

IJRTE

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Community detection is a nowadays research problem in the Big Data era related to huge volume, variety, and velocity of data. Big data defines data where normal processing, storage, retrieval fails and require some advanced tools to solve these types of problem. An important tool in the analysis of complex network is community detection. Community detection or community mining is a technique which is used to find the same type of relations in a particular group. Community detection is also known as Graph Clustering. This paper represents Big data in the form of graphs and detects community via some graph algorithms like METIS, Spectral Partitioning, hierarchical clustering, Markov Clustering, Genetic Algorithm based community detection algorithm, etc. Community detection is widely used in various types of disease detection, drug formation, species clustering. It can be also used in social networking sites to control crimes by detecting community bad peoples.

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A Multi-Population Cultural Algorithm for Community Detection in Social Networks

Cited by 51 publications

References 10 publications

On The Use of Optimization for Mining Clusters in Networks

On The Use of Optimization for Mining Clusters in Networks

Review of Community Detection over Social Media:Graph Prospective

Community Detection Algorithms for Big Data using Graph Theory

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