Advancing community detection using Keyword Attribute Search

Chobe, Sanket; Zhan, Justin

doi:10.1186/s40537-019-0243-y

Cited by 11 publications

(3 citation statements)

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“…Attributed Community Search. Community search over attributed graphs problem has attracted much attention in recent years [9], [11], [17], [18], [25], [46], [49]. The community search problem for a profiled graph was investigated in [9].…”

Section: Related Workmentioning

confidence: 99%

“…Each CPtree node corresponds to a label and stores the k-cores sharing this label. The keyword search-based method was proposed in [11] to generate a community with a structure and attribute cohesiveness. First, the influential attributes are derived according to the probability of occurrence of each pair of attributes type-value to all nodes and edges.…”

Section: Related Workmentioning

confidence: 99%

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Influential Attributed Communities Search in Large Networks (InfACom)

2021

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Community search is a fundamental problem in graph analysis. In many applications, network nodes have specific properties that are essential for making sense of communities. In these networks, attributes are associated with nodes to capture their properties. The community influence is a key property of the community that can be employed to sort the communities in a network based on the relevance/importance of certain attributes. Unfortunately, most of the previously introduced community search algorithms over attributed networks neglected the community influence. In this paper, we study the influential attributed community search problem. Different factors for measuring the influence are discussed. Also, different Influential Attributed Community (InfACom) algorithms based on the concept of k-clique are proposed. Two techniques are presented one for sequential implementation with three variations and one for parallel implementation. In addition, we propose efficient algorithms for maintaining the proposed algorithms on dynamic graphs. The proposed algorithms are evaluated on different real datasets. The experimental results show that the summarization technique reduces the size of the graph by approximately half. In addition, it shows that the proposed algorithms EnhancedExact and Approximate outperform the state-of-the-art approaches Incremental Time efficient (Inc − T ), Incremental Space efficient (Inc − S), Exact, and 2-Approximation (AppInc) in both efficiency and effectiveness. For the EnhancedExact algorithm, the results show that the efficiency is at least 7 times faster than the Inc − S algorithm, at least 4.5 times faster than the Inc − T algorithm, and 2 times faster than the AppInc algorithm. For the Approximate algorithm, the results show that its efficiency is at least 10 times faster than the Inc − S algorithm, at least 6.4 times faster than the Inc − T algorithm, and 3 times faster than the AppInc algorithm. Finally, the results show that the proposed algorithms retrieve cohesive communities with a smaller diameter than all the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Influential Attributed Communities Search in Large Networks (InfACom)

2021

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“…It is based on the concept of module-based network architecture. The modularity score is used to detect the strength of a community in the network(Chobe & Zhan, 2019).…”

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confidence: 99%

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

Selvaraj¹,

Murugasamy²

2022

AIP

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In recent years, community detection is important because members of the same community share the same concepts. For efficient community detection in a social network, the influence node plays a vital role. A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community. Therefore, the status of a person is determined by the user's influence strength. That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network. Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another. Effective community detection in a complex structure is a challenging task. Many studies have been done based on topological networks. The approaches are ineffective, inefficient and require more time to process. To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO). Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.

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