Local Community Detection With the Dynamic Membership Function

Luo, Wenjian; Zhang, Daofu; Jiang, Hao; Li, Ni; Hu, Yamin

doi:10.1109/tfuzz.2018.2812148

Cited by 62 publications

(28 citation statements)

References 30 publications

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“…In [7], the authors proposed a three-phase method to detect the local community to which a network node belongs, without requiring the global information of the network. In each phase, the dynamic membership function depends on the number of nodes from the local community, and which neighboring nodes belong to the community is determined by a formula.…”

Section: Background and Related Workmentioning

confidence: 99%

Dynamic Membership Functions for Context-Based Fuzzy Systems

et al. 2021

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In fuzzy systems, membership functions determine the groups to which a variable can belong to, and these groups are static or only have one setting in some aspect. However, fuzzy systems typically require to model the dynamic environment they represent. Still, this behavior does not reflect the membership groups in a conventional way. Thus, conventional fuzzy systems are not capable of reflecting the dynamics of the real-time context. The approach presented consists of a fuzzy system where the membership functions can have dynamic transformations, according to contextual variables that influence them, to have a model that adjusts in real time. The membership functions' dynamism is achieved because the form in the sets can be transformed; the maximum degree of membership of a set is in a range of zero to one; and, the location of the sets in the discourse universe can vary dynamically. The results show the feasibility of a context-based fuzzy system with dynamic membership functions built-in real time, that has been influenced by contextual variables. Therefore, unlike other proposals, this approach allows modeling the influence of the context on a fuzzy system, making it more adjusted to reality. To illustrate our proposed approach, a case study is presented where a fuzzy system estimates the heat stress in a work environment that uses data acquired from wearable devices. This system automatically generates the following indicators: (i) energy level wasted while performing a physical activity, (ii) personalized measurement of workload level, and (iii) measurement of Occupational Heat Stress (OHS).

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Section: Background and Related Workmentioning

confidence: 99%

Dynamic Membership Functions for Context-Based Fuzzy Systems

et al. 2021

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“…end if 48: end while 49: return C n is the number of nodes, k is the average degree of the nodes, k max is the maximum degree of the nodes, c min is the minimum number of members in the community, c max is the maximum number of members in the community, µ is mixing parameter that control the fuzziness of the community, t 1 is minus exponent for the degree sequence, t 2 is minus exponent for the community size distribution, on is the number of overlapping nodes, and om is the number of memberships of the overlapping nodes. By setting the same parameters as in [52], four small networks and two large networks are generated as follows: • LFR_S4: n = 1000, k = 7, k max = 25, c min = 10, c max = 300, µ = 0.1, t 1 = 2, t 2 = 1, on = 0, om = 0.…”

Section: A Random Networkmentioning

confidence: 99%

“…So far, we have followed up to the local community detection algorithms that include DMF_M1, DMF_M2, and DMF_M proposed in [52], and the M algorithm proposed in [21]. To compare the performance of these algorithms, experiments are carried out by using the aforementioned generated networks, and the comparison of the experimental results given by each algorithm are shown in Table 1.…”

Section: A Random Networkmentioning

confidence: 99%

Community Detection Based on Local Information and Dynamic Expansion

et al. 2019

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Mining the community structure in the real-world networks has been a hot topic in the field of complex networks, and has emerged as a prominent research area and continues to grow with the introduction of requirements for personalized recommendation. However, most of the existing community detection algorithms are based on global information, fewer works are devoted to detecting the communities hidden in the network by using local information. To this end, in this paper, we propose two improved signed modularity functions to evaluate the community properties in complex network, and then we apply these indicators to identify the community structure by using the local information since it is difficult to obtain the global information in practice. During the dynamic expansion, each local community will absorb the neighboring node with the highest positive energy; in addition, a new local community is generated when all local communities cannot contain the neighboring node. Finally, the algorithm has been applied to unsigned networks and signed networks, respectively. The experimental results show that the division results given by our proposed algorithm are in line with the actual ones in artificial and real-life networks.

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“…Then a CS-based detection algorithm was proposed to detect the intercommunity links. Luo et al [52] analyzed the formation of the local community and proposed two local community detection algorithms based on the dynamic membership function. Each of the algorithms is divided into three stages: 1) the initial stage, 2) the middle stage, and 3) the closing stage.…”

Section: B Community Detection Of Mobile Social Networkmentioning

confidence: 99%

Social Community Detection Scheme Based on Social-Aware in Mobile Social Networks

Dian-xing

Wang

2019

IEEE Access

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Currently many factors can influence community detection in mobile social networks, where node mobility is a key factor to influence the stability of community structure. In this paper, we propose a social community detection scheme for mobile social networks based on social-ware, including social attribute similarity, node interest similarity and node mobility. Compared with other community detection schemes, our proposed scheme can accurately detect the communities based on community attribute and node mobility. The experiments show the numbers of detected communities and members in the maximal size community generated by our scheme are both smaller than those of the GN and NM schemes. Additionally, since the nodes (users) both have higher mobility in mobile social networks, the efficiency of our proposed scheme relatively becomes higher. The experiments show when the values of mobility in the test data sets increase, the running time of our proposed scheme decreases when the number of edges is fixed. For example, the running time of our proposed scheme is about 17s when the maximum value of mobility is set as 0.5 and the number of edges is about 16000, and further the running time is only about 13s when the maximum value of mobility is set as 1. Therefore, our proposed scheme can more accurately and efficiently make community detection to increase the stability of mobile community structure. INDEX TERMS Mobile social network, social ware, node mobility, community detection.

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Local Community Detection With the Dynamic Membership Function

Cited by 62 publications

References 30 publications

Dynamic Membership Functions for Context-Based Fuzzy Systems

Dynamic Membership Functions for Context-Based Fuzzy Systems

Community Detection Based on Local Information and Dynamic Expansion

Social Community Detection Scheme Based on Social-Aware in Mobile Social Networks

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