A Community Detecting Algorithm in Directed Weighted Networks

Liu, Hongtao; Qin, Xiao Hong; Hongfeng, Yun; Wu, Yu Mao

doi:10.1007/978-3-642-21765-4_2

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…. As we know, if an individual has a lot of direct contacts with others in complex systems, it is more important and has great "power" [17]. Under the guidance of this idea, our algorithm for detecting communities in directed and weighted networks is proposed.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Community Detection in Real Large Directed Weighted Networks

Liu¹,

Qin²,

Liu³

et al. 2013

JDCTA

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In this paper, the impact factors of edge weight and vertex weighted degree are introduced into community detection, and the directed weighted degree is used to measure the importance of the node. Based on the modularity optimization, a new community detecting algorithm with flexible and multitask architecture for directed and weighted networks is proposed. Then the community detection on the real large mail network is conducted, and the experimental results demonstrate that in large directed and weighted networks, the proposed algorithm is efficient with shorter execution time. By comparing the detecting results under different network types, we conclude that the use of weights can improve partition effectiveness and accuracy.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Community Detection in Real Large Directed Weighted Networks

Liu¹,

Qin²,

Liu³

et al. 2013

JDCTA

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show abstract

“…Community Detection is a well-studied problem in monoplex (single network) analysis. A large number of competitive approximation algorithms exist (see reviews in [25], [26]) including algorithms for [27], weighted [28], dynamic networks [29], as well as parallel algorithms [30], [31].…”

Section: Related Workmentioning

confidence: 99%

Efficient Community Detection in Boolean Composed Multiplex Networks

Santra,

Bhowmick,

Chakravarthy

2019

Preprint

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Networks (or graphs) are used to model the dyadic relations between entities in a complex system. In cases where there exists multiple relations between the entities, the complex system can be represented as a multilayer network, where the network in each layer represents one particular relation (or feature). The analysis of multilayer networks involves combining edges from specific layers and then computing a network property.Different subsets of the layers can be combined. For any Boolean combination operation (e.g. AND, OR), the number of possible subsets is exponential to the number of layers. Thus recomputing for each subset from scratch is an expensive process. In this paper, we propose to efficiently analyze multilayer networks using a method that we term network decomposition.Network decomposition is based on analyzing each network layer individually and then aggregating the analysis results. We demonstrate the effectiveness of using network decomposition for detecting communities on different combinations of network layers. Our results on multilayer networks obtained from realworld and synthetic datasets show that our proposed network decomposition method requires significantly lower computation time while producing results of high accuracy.

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Community detection in directed weighted networks using Voronoi partitioning

Molnár,

Márton,

Horvát

et al. 2024

Sci Rep

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Community detection is a ubiquitous problem in applied network analysis, however efficient techniques do not yet exist for all types of network data. Directed and weighted networks are an example, where the different information encoded by link weights and the possibly high graph density can cause difficulties for some approaches. Here we present an algorithm based on Voronoi partitioning generalized to deal with directed weighted networks. As an added benefit, this method can directly employ edge weights that represent lengths, in contrast to algorithms that operate with connection strengths, requiring ad-hoc transformations of length data. We demonstrate the method on inter-areal brain connectivity, air transportation networks, and several social networks. We compare the performance with several other well-known algorithms, applying them on a set of randomly generated benchmark networks. The algorithm can handle dense graphs where weights are the main factor determining communities. The hierarchical structure of networks can also be detected, as shown for the brain. Its time efficiency is comparable or even outperforms some of the state-of-the-art algorithms, the part with the highest time-complexity being Dijkstra’s shortest paths algorithm ($${\mathcal {O}}(|E| + |V|\log |V|)$$ O ( | E | + | V | log | V | ) ).

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A Community Detecting Algorithm in Directed Weighted Networks

Cited by 3 publications

References 5 publications

Community Detection in Real Large Directed Weighted Networks

Community Detection in Real Large Directed Weighted Networks

Efficient Community Detection in Boolean Composed Multiplex Networks

Community detection in directed weighted networks using Voronoi partitioning

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