Modularity based mobility aware community detection algorithm for broadcast storm mitigation in VANETs

Kamakshi, S.; Sriram, V. S. Shankar

doi:10.1016/j.adhoc.2020.102161

Cited by 16 publications

(3 citation statements)

References 13 publications

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“…This technique exploits community detection in real-time by eliminating the nodes and edges and including the nodes and edges in the network. Kamakshi et al [13] proposed solving the broadcast storm problems in the Vehicular Ad hoc Network (VANET) using clustering techniques to detect community. The social media platform can create a less dynamic or static network architecture using the clustering technique.…”

Section: Review Of Literaturementioning

confidence: 99%

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Devi¹,

Rajalakshmi²

2023

ITC

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Due to technological development, social media platforms like forums and microblogs allow people to share their experiences, thoughts, and feelings. The organization, shopping groups etc. has major discussions regarding their business advertisements and product reviews. Also, there are certain followers for particular person or group due to their interests. Here the major issue is to know who or which group in social media is more influenced. The social media analysis needs to perform for identifying influenced person in the social media. The influencer node/person detection in a certain community is already done using greedy algorithm, genetic algorithm, ant colony optimization, cuckoo search algorithms. These existing techniques takes more time for diffusion and accuracy in prediction is not satisfied by users. To overcome this issues, in this research influencer node is identified using optimized Girvan Newman Cuckoo Search Algorithm (GNCSA). First Grivan Newman is used to identify the community and perform community detection. Cuckoo search algorithm uses host bird strategy in finding cuckoo eggs in his nest. Based on the centrality measure it decides whether the node is an influencer or not. This paper proposed Influencer detection by forming community first and measures angular centrality using optimized Girvan Newman cuckoo search algorithm. Our proposed work GNCSA gives a better accuracy rate for the data sets of Dolphin 0.89, for Facebook dataset got 0.93, Twitter data set got 0.94 and for YouTube data set 0.92, karate club and football got 0.91. This proposed work increases the intracommunity of the social network and improves its performance accurately by detecting the influencer in the social network.

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Section: Review Of Literaturementioning

confidence: 99%

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Devi¹,

Rajalakshmi²

2023

ITC

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“…The advancements in network science have propelled the analysis of complex networks into a prominent and burgeoning question within academia [1][2][3][4][5][6]. Within this realm of study, the identification of key nodes has persistently remained a crucial and foundational issue.…”

Section: Introductionmentioning

confidence: 99%

Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information

Wang,

Liu

et al. 2023

New J. Phys.

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Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations.

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“…(Keyvanpour et al, 2020) proposed an anomaly recognition method of social network graph based on community detection (AD-C) and applied it to identify anomalies in social networks. (Kamakshi and Sriram, 2020) used the community detection algorithm to form a stable group of vehicles, reduced the system overhead and time delay, and improved the safety, tra c e ciency and convenience of vehicles and roads. In addition, community detection and link prediction technology are also widely employed in the detection and recommendation system of Telecom fraud organizations in the real world (Jia and Rongjian, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Deformation Characteristics of Reverse Slope Under The Influence of Reservoir Water Based On Community Detection

Xie

Cui

Qin

et al. 2021

Preprint

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In order to study the deformation characteristics of reverse slope, this paper took the slope of Xiaodongcao as the research object, applied the Louvain community detection algorithm, considered the influence of reservoir water level change, partitioned the slope deformation characteristics. The deformation characteristic zoning result was superimposed with the slope displacement cloud map and three types of geological geometric characteristic factor zoning map obtained by ArcGIS. The results show that：Community detection can quickly identify the closely connected part of slope network, and the specific location of this part is affected by reservoir water. After the community detection result is superimposed with the displacement cloud map, the areas with large deformation and close connection in the slope can be identified. It is found that the community with severe deformation have at least 5% more displacement and up to 21% more displacement than that with slow deformation. In addition, the location of leader nodes can be identified, and the number of leader nodes does not exceed 20% of the total nodes in the community, and its average displacement is at least 10% more than that of ordinary nodes, up to 36%. After the community detection result is superimposed with the zoning map of slope grade, it can be concluded that the slope grade within the community with severe deformation is greater than 60°, indicating that the larger slope grade is more sensitive to the bank slope deformation.

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Modularity based mobility aware community detection algorithm for broadcast storm mitigation in VANETs

Cited by 16 publications

References 13 publications

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information

Analysis of Deformation Characteristics of Reverse Slope Under The Influence of Reservoir Water Based On Community Detection

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