Overlapping community detection in heterogeneous social networks via the user model

Huang, Mingqing; Zou, Guobing; Zhang, Bofeng; Liu, Yue; Gu, Yajun; Jiang, Keyuan

doi:10.1016/j.ins.2017.11.055

Cited by 44 publications

(20 citation statements)

References 24 publications

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“…Theoretically, the network is represented by a graph G = (V, E) where V the set of nodes representing the individuals and E the set of edges representing the interactions between them. Figure 1 shows an example of a network with V = 10 (0, 1, 2, 3,4,5,6,7,8,9) and E the set of edges connecting nodes like (1 − 7, 6 − 7, 3 − 5, 3 − 4, ...).…”

Section: Backgrounds Of Community Detection In Networkmentioning

confidence: 99%

“…Disjoint methods build a disjoint partitioning of the network where each actor is assigned to only one community. However, given that actors usually operate in several communities such as in the research domain where professors usually collaborate with several researchers in different fields or in social domains where a person has his family group as well as friends group at the same time, assigning an actor to only one community do not reflect real structures in the network [6,7]. Nondisjoint community detection methods solve this issue and allow actors to be assigned to one or several communities resulting in an overlapping partitioning of the network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disjoint and Non-Disjoint Community Detection with Control of Overlaps Between Communities

N’cir

Maiza²,

Bouaguel

et al. 2021

SN COMPUT. SCI.

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Overlapping community detection has become an important challenge in networks analysis that motivates researchers to propose community detection methods that best fit existing complex and non-disjoint structures in real-world networks such as social, scientific and collaborative networks. Existing overlapping community detection methods usually build large overlaps between communities, larger than expected, and do not allow users to interact with the system to regulate this size, except those allowing to include hard constraints. To solve these issues, we propose a novel non-disjoint community detection method, referred to as CDCO, which easily allows users to interact with the system and regulate overlaps between communities based on existing relationships between nodes in the network. In the same way that allowing to analysts to control the number of communities or the minimal number of actors in the community, CDCO allows to regulate overlaps using an parameter which can favor or penalize overlaps. The regulation of overlaps is introduced in the objective criterion and optimized iteratively during the community detection process. Extensive experiments, conducted on both simulated and real-world networks having different sizes of overlaps, show the importance of the regulation of overlaps when a nondisjoint partitioning of the network is needed and show that CDCO outperforms existing conventional methods in terms of both F-measure and NMI.

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Section: Backgrounds Of Community Detection In Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disjoint and Non-Disjoint Community Detection with Control of Overlaps Between Communities

N’cir

Maiza²,

Bouaguel

et al. 2021

SN COMPUT. SCI.

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“…Also, Moosavi et al (2017) have proposed an approach based on the interests, frequent patterns, actions and finally activities of users on networks. A year later, 2018, many other models have been proposed like that of Huang et al (2018) who proposed a new model of detection of overlapping communities in heterogeneous SNs via the user model. Also, Ahajjam et al (2018) proposed a new scalable leader-community detection approach.…”

Section: Background and Related Workmentioning

confidence: 99%

A new model for communities' detection in dynamic social networks inspired from human families

Djerbi

Amad

Imache

2020

IJITST

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Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature. However, most solutions have common problems: the stability and the community structures quality. In this paper, we propose a new model to find communities based on a new concept called 'large families'. This model will be used, to motivate a community detection strategy to identify and effectively monitor the evolution of dynamic communities. We propose a compromise between the stability and the quality metrics. We apply our model on a real social network of the karate club of Zachary. Also, we describe experiences of our model on a large scale network of Enron's email data set as broader Benchmark Network. Simulations results show that our proposed model is globally satisfactory.

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“…Experimental results show that the model is more competitive than commonly used models. Huang et al [11] built a user model of heterogeneous networks with undirected and directed edges and applied the model to propose a new approach to overlapping community detection in heterogeneous social networks (OCD-HSN). Compared with the existing state-of-the-art algorithms, this method shows higher accuracy and lower time consumption under the real social network.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Elmongui et al [7], Qian et al [8], Eirinaki et al [9], and Jiang et al [10] each proposed a recommendation service method based on user interests. Huang et al [11], Bhattacharya et al [12], Zarrinkalam et al [13], [14], and Li et al [15] focused on interest modeling for Internet users for different goals and tasks. Moreover, Kapanipathi et al [16], Xu et al [17], and Piao and Breslin [18] focused on Interest mining for Internet users based on access logs, microblog/blog accessing, and content and behavior of browsing, respectively.…”

Section: Introductionmentioning

confidence: 99%

Association Rules Mining Among Interests and Applications for Users on Social Networks

Zhou

Chen

et al. 2019

IEEE Access

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Interest is an important concept in psychology and pedagogy and is widely studied in many fields. Especially in recent years, the widespread use of many interest-based recommendation systems has greatly promoted research on interest modeling and mining on social networks. However, the existing studies have rarely tried to explore the relationships among interests and their application value, and most similar studies analyze user behavior data. In this paper, we propose and verify two hypotheses about the interests of social network users. We then use association rules to mine users' interests from LinkedIn users' profiles. Finally, based on the interest association rules and user interest distribution on Twitter, we design an approach to mine interests for Twitter users and conduct two experiments to systematically demonstrate the approach's effectiveness. According to our research, we found that there are a large number of association rules between human interests. These rules play a considerable role in our method of interest mining. Our research work not only provides new ideas for interest mining but also reveals the internal relationship between interest and its application value. The research work has certain theoretical and practical value. INDEX TERMS Interests, correlation analysis, association rules, interest mining.

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Overlapping community detection in heterogeneous social networks via the user model

Cited by 44 publications

References 24 publications

Disjoint and Non-Disjoint Community Detection with Control of Overlaps Between Communities

Disjoint and Non-Disjoint Community Detection with Control of Overlaps Between Communities

A new model for communities' detection in dynamic social networks inspired from human families

Association Rules Mining Among Interests and Applications for Users on Social Networks

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