Label propagation algorithm for community detection based on node importance and label influence

Zhang, Xiankun; Ren, Jing; Chen, Song; Jia, Jia; Zhang, Qian

doi:10.1016/j.physleta.2017.06.018

Cited by 82 publications

(32 citation statements)

References 17 publications

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“…The effective data transmission algorithm based on social relationships in opportunistic mobile social networks proposed by us addresses a data-forwarding method of multiple copies. While the problem of large numbers of data copies exists in traditional algorithms, our research proposes a community-based forwarding strategy [25], which can effectively reduce the number of data copies. Compared with other algorithms, this work does not adopt the method of transmitting messages between single nodes but proposes a scheme of transmitting information through efficient communities.…”

Section: Related Workmentioning

confidence: 99%

An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks

Yan

Chen

et al. 2018

Algorithms

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Abstract:With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.

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Section: Related Workmentioning

confidence: 99%

An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks

Yan

Chen

et al. 2018

Algorithms

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“…Furthermore, the use of inductive setting of FME algorithm to estimate the labels of unseen data can be adopted to extend the proposed method to large scale databases. As we mentioned before, label propagation has different applications in community detection [16], image segmentation [17], and clustering [18], and classification [19] tasks. Therefore, in our future works, we will focus on applying the proposed method in the aforementioned applications.…”

Section: Discussionmentioning

confidence: 99%

“…This aims to propagate the label of labeled data to unlabeled ones over the graph [15]. LP has different applications in community detection [16], image segmentation [17], clustering [18], and classification [19] tasks. Although most of the algorithms use one single graph as an input of the LP algorithms, such as the Zhou method [20], flexible manifold embedding (FME) [21], local and global consistency (LGC) [22], and Gaussian fields and harmonic function (GFHF) [23], exploiting various similarity graphs can enhance the performance of LP process (graph-fusion methods) [15,[24][25][26][27][28], thereby creating multiple similarity graphs where each contains complementary information of data and fusing them together can lead to a better representation of data.…”

Section: Introductionmentioning

confidence: 99%

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

2019

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In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms.

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“…On one hand, the node update order is random, on the other hand, when the neighbors have more than one maximum number of the labels, the node randomly selects one of the labels. Although some scholars have made improvements to LPA from various aspects [25][26][27], there is no algorithm that considers label number, label history, and other information comprehensively when selecting labels. There are also some superior dynamic system-based algorithms such as synchronization [28], distance dynamics [29], and so on.…”

Section: Related Workmentioning

confidence: 99%

Community Detection Based on a Preferential Decision Model

et al. 2020

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The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

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Label propagation algorithm for community detection based on node importance and label influence

Cited by 82 publications

References 17 publications

An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks

An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

Community Detection Based on a Preferential Decision Model

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