NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Huang, Bingyang; Wang, Chaokun; Wang, Binbin

doi:10.3390/s19020260

Cited by 27 publications

(9 citation statements)

References 39 publications

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“…Synthetic networks and the true communities were generated by the LFR benchmark [42]. The methods in [43] were used to generate each network's attribute matrix according to its true communities. Specifically, the vertices in a community shared the same attributes with high probability while the probability for the vertices in different communities is low.…”

Section: Datasetsmentioning

confidence: 99%

Overlapping Community Detection Based on Attribute Augmented Graph

Lin

Zhan

Zhao

et al. 2021

Entropy

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There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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Section: Datasetsmentioning

confidence: 99%

Overlapping Community Detection Based on Attribute Augmented Graph

Lin

Zhan

Zhao

et al. 2021

Entropy

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“…To overcome this shortage, many researches have focused on detecting communities in homogeneous networks based on semantic information and nodes attributes 15 . There are four categories of approaches for semantic community detection in social networks, 16 depending on their input: a clustering‐based approach, 12-14,17 a model‐based approach, 15-19 a label propagation based approach, 20,21 and a graph‐based approach 7,22-25 …”

Section: Related Workmentioning

confidence: 99%

Multidimensional community discovering in heterogeneous social networks

Guesmi

Trabelsi

Latiri

2020

Concurrency and Computation

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Summary Multidimensional community discovering in heterogeneous social networks is an important issue. Many approaches have been proposed for community discovering in heterogeneous networks. However, they have focused only on topological properties of these networks, ignoring the embedded semantic information. As the solution to this information glut limit, we propose, in this article, a new multidimensional community discovering approach, which incorporates the multiple types of objects and relationships, derived from a heterogeneous networks. First, we propose to construct the concept lattice family CLF to represent the different objects and relations of the heterogeneous social networks based on the relational concept analysis techniques. Then after we introduce a new algorithm that explores such CLF and extract the multidimensional communities. Carried out experiments on real datasets enhance the effectiveness of our proposal and open promising issues.

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“…Label propagation methods firstly initialize the label of each node, then update the labels iteratively, and finally determine the communities by the label distribution. Typical label propagation methods include LPA (Raghavan, Albert, and Kumara 2007), SLPA (Xie, Szymanski, and Liu 2011), NMLPA (Huang, Wang, and Wang 2019), and so on.…”

Section: Related Workmentioning

confidence: 99%

Forbidden Nodes Aware Community Search

Wang

Zhu

2019

AAAI

Self Cite

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Community search is an important problem in network analysis, which has attracted much attention in recent years. It starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. In this paper, we argue that there are many real scenarios where some nodes are not allowed to appear in the community. Then, we introduce a new concept called forbidden nodes and present a new problem of forbidden nodes aware community search to describe these scenarios.To address the above problem, three methods are proposed, i.e., k-core based FORTE (Forbidden nOdes awaRe communiTy sEarch), k-truss based FORTE and CW based FORTE, where the effects of both forbidden nodes and query nodes are thoroughly considered for each node in the resultant community. The former two methods are able to make use of popular community structures, while the latter is based on a new metric called weighted conductance. The extensive experiments conducted on real data sets demonstrate the effectiveness of the proposed methods.

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NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Cited by 27 publications

References 39 publications

Overlapping Community Detection Based on Attribute Augmented Graph

Overlapping Community Detection Based on Attribute Augmented Graph

Multidimensional community discovering in heterogeneous social networks

Forbidden Nodes Aware Community Search

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