Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition

Zhang, Aiping; Ren, Guang; Lin, Yejin; Jia, Bin; Cao, Hui; Zhang, Jundong; Zhang, Shubin

doi:10.1155/2014/148686

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Liu et al introduce one specilaized algorithm based on the advanced model LPAm+ [16], [17]. Moreover, Zhang et al continue to ameriolate into LPAp [18]. However, the researches above mostly solve the issue of discovering the direct community structure on graph whereas very few researches focus on minimizing the calculating space regarding to node and edge number in a graph so as to shorten analysis time as well as quick discovery of community social network and increasing efficiency in community discovery in social networks.…”

Section: Label Propagation Algorithmmentioning

confidence: 99%

A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

2019

ijeat

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Identifying communities has always been a fundamental task in analysis of complex networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. Amongst them, the label propagation algorithm (LPA) brings great scaslability together with high accuracy but which is not accurate enough because of its randomness. In this paper, we study the equivalence properties of nodes on social network graphs according to the labeling criteria to shorten social network graphs and develop label propagation algorithms on shortened graphs to discover effective social networking communities without requiring optimization of the objective function as well as advanced information about communities. Test results on sample data sets show that the proposed algorithm execution time is significantly reduced compared to the published algorithms. The proposed algorithm takes an almost linear time and improves the overall quality of the identified community in complex networks with a clear community structure.

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Section: Label Propagation Algorithmmentioning

confidence: 99%

A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

2019

ijeat

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“…LPAm+ combines LPAm with multistep greedy agglomerative algorithm to get higher modularity values. Thus, LPAm+ doest not guarantee near-linear time complexity [16]. Xing et al presented a node influence based label propagation algorithm called NIBLPA [17].…”

Section: )mentioning

confidence: 99%

“…NIBLPA defines two concepts node influence and label influence for specifying node orders and label choosing mechanism respectively. Zhang et al proposed a label propagation algorithm with prediction of percolation transition named LPAp [16]. They transformed the process of label propagation into network construction process.…”

Section: )mentioning

confidence: 99%

Efficient Community Detection Algorithm with Label Propagation using Node Importance and Link Weight

Arab¹,

Hasheminezhad²

2018

ijacsa

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Abstract-Community detection is a principle tool for analysing and studying of a network structure. Label Propagation Algorithm (LPA) is a simple and fast community detection algorithm which is not accurate enough because of its randomness. However, some advanced versions of LPA have been presented in recent years, but their accuracy need to be improved. In this paper, an improved version of label propagation algorithm for community detection called WILPAS is presented. The proposed algorithm for community detection considers both nodes and links important. WILPAS is a parameter-free algorithm and so requires no prior knowledge. Experiments and benchmarks demonstrate that WILPAS is a pretty fast algorithm and outperforms other representative methods in community detection on both synthetic and real-world networks. More specifically, experiments show that the proposed method can detect the true community structure of real-world networks with higher accuracy than other representative label propagation-based algorithms. Finally, experimental results on the networks with millions of links reveal that the proposed algorithm preserve nearly linear time complexity of traditional LPA. Therefore, the proposed algorithm can efficiently detect communities of large-scale social networks.

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“…Note that the algorithm stops when there is no active node. Zhang et al [ 13 ] proposed a modified LPA with the capability for prediction of a percolation transition (LPAp). The effect of the prediction part in the LPAp will be to delay the formation of a monster size community.…”

Section: Introductionmentioning

confidence: 99%

Detecting Community Structure by Using a Constrained Label Propagation Algorithm

Chin

Ratnavelu

2016

PLoS ONE

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Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA). The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR), Relaxed Caveman (RC) and Girvan-Newman (GN) benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results.

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Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition

Cited by 5 publications

References 28 publications

A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

Efficient Community Detection Algorithm with Label Propagation using Node Importance and Link Weight

Detecting Community Structure by Using a Constrained Label Propagation Algorithm

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