A new scalable leader-community detection approach for community detection in social networks

Ahajjam, Sara; Haddad, Mohamed El; Badir, Hassan

doi:10.1016/j.socnet.2017.11.004

Cited by 71 publications

(39 citation statements)

References 19 publications

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“…Discovery. Every individual says different words as each node has its own information contents in semantic social network [28]. So we abstract semantic context into topic, and then we extract keywords from topic.…”

Section: Similar Semantic Informationmentioning

confidence: 99%

A Semantic Community Detection Algorithm Based on Quantizing Progress

2019

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The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.

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Section: Similar Semantic Informationmentioning

confidence: 99%

A Semantic Community Detection Algorithm Based on Quantizing Progress

2019

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“…Furthermore, considering the literature of community detection, in [5], they proposed a novel modularity function to improve the community detection in social networks. In [6], they provided a new approach for community detection in social networks using leader nodes which their algorithm has two steps. First, they detect the leaders.…”

Section: Introductionmentioning

confidence: 99%

An adaptive hybrid algorithm for social networks to choose groups with independent members

Hadikhani¹,

Hadikhani

2020

Evol. Intel.

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Choosing a committee with independent members in social networks can be named as a problem in group selection and independence in the committee is considered as the main criterion of this selection. Independence is calculated based on the social distance between group members. Although there are many solutions to solve the problem of group selection in social networks, such as selection of the target set or community detection, just one solution has been proposed to choose committee members based on their independence as a measure of group performance. In this paper, a new adaptive hybrid algorithm is proposed to select the best committee members to maximize the independence of the committees. This algorithm is a combination of particle swarm optimization (PSO) algorithm with two local search algorithms. The goal of this work is to combine exploration and exploitation to improve the efficiency of the proposed algorithm and obtain the optimal solution. Additionally, to combine local search algorithms with particle swarm optimization, an effective selection mechanism is used to select a suitable local search algorithm to combine with particle swarm optimization during the search process. The results of experimental simulation are compared with the well-known and successful metaheuristic algorithms. This comparison shows that the proposed method improves the group independence by at least 21%.

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“…The degree of centrality is obtained by two approaches, global and local methods. The global method emphasizes all aspects of actor interaction (betweenness centrality), while the local method focuses on the position of the actor (degree centrality) [16].…”

Section: Introductionmentioning

confidence: 99%

Student Performance Based on Activity Log on Social Network and e-Learning

Agusriandi¹,

Sitanggang²,

Wijaya³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.

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A new scalable leader-community detection approach for community detection in social networks

Cited by 71 publications

References 19 publications

A Semantic Community Detection Algorithm Based on Quantizing Progress

A Semantic Community Detection Algorithm Based on Quantizing Progress

An adaptive hybrid algorithm for social networks to choose groups with independent members

Student Performance Based on Activity Log on Social Network and e-Learning

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