GA-LP: A genetic algorithm based on Label Propagation to detect communities in directed networks

Francisquini, Rodrigo; Rosset, Valério; Nascimento, Mariá C. V.

doi:10.1016/j.eswa.2016.12.039

Cited by 21 publications

(9 citation statements)

References 23 publications

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“…Xing et al [15] proposed NIBLPA, which updates node's label based on fixed node orders of labels updated in the descending order of node importance. In another paper, R. Francisquini [16] tries to use a meta-heuristic algorithm and introduces GA-LP, that its main idea is the local nature of the key operators of the genetic algorithm. X. Zhang et al [17] represent LPA-NI which is label propagation algorithm for detecting communities based on node importance and label influence.…”

Section: Related Workmentioning

confidence: 99%

Weighted label propagation based on Local Edge Betweenness

2019

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In complex networks, especially social networks, networks could be divided into disjoint partitions that the ratio between the number of internal edges (the edges between the vertices within same partition) to the number of outer edges (edges between two vertices of different partitions) is high.Generally, these partitions are called communities. Detecting these communities helps data scientists to extract meaningful information from graphs and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. However, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. In this paper, we have tried to improve Label Propagation Algorithm by using edge betweenness metric, so that it is able to identify distinct communities in both real world and artificial networks in near linear time complexity with acceptable accuracy. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable; additionally, the proposed algorithm is scalable.

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Section: Related Workmentioning

confidence: 99%

Weighted label propagation based on Local Edge Betweenness

2019

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“…In this paper, a crossover operator proposed in [40] was adopted to generate the offspring. The pseudo-code was described in the second part of Algorithm 2 (Lines 6-20).…”

Section: B Population Evolutionmentioning

confidence: 99%

“…In this section, the performance of LPMOEA was compared with other four representative algorithms, including Infomap [27], directed Louvain [16], directed label propagation algorithm (DLPA) [38], and GA-LP [40]. The four algorithms were selected for comparison as they are either state-of-the-art methods for community detection in directed networks or relevant to LPMOEA in some sense.…”

Section: Experiments On Synthetic Networkmentioning

confidence: 99%

A Multi-Objective Community Detection Algorithm for Directed Network Based on Random Walk

Wen

Lin

2019

IEEE Access

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Uncovering community structure is an important technique for studying complex networks. While a large bulk of algorithms have been proposed for community detection in recent years, most of them were designed for undirected networks. Considering many real-world networks are by nature directed, it is necessary to develop community detection methods that can handle directed networks. In this work, we formulates a multi-objective framework for community detection in directed networks and proposes a multi-objective evolutionary algorithm for finding efficient solutions under this framework. Specifically, based on the theory that an efficient partition of directed networks should have larger network information flow within the community than that between different communities, we first designed two conflicting objective functions based on PageRank random walk, one of which is to maximize withincommunity transition probability, and the other is to minimize between-community transition probability. By optimizing these two objectives simultaneously, we modelled the problem of community detection as a multi-objective optimization problem, and then developed a novel multi-objective evolutionary algorithm to solve it. Particularly, to guarantee the capability of searching the optimal solution, our proposed method designed/adopted the directed-network-specific population initialization method and evolutionary operator by introducing label propagation algorithm into multi-objective genetic algorithm. Comparison with other four art-of-the-state algorithms, our method showed the competitive performance on both synthetic and realworld networks. Moreover, attributing to the multi-objective framework, the proposed method could generate multiple optimal network partitions in a single run, which provides a hierarchical description of community structure of the network.

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“…Since the modularity measure was proposed by Newman and Girvan (2004), several authors have studied ways to detect community structures in a plethora of complex networks. In particular, community detection algorithms that aim at maximizing the modularity measure have been extensively studied (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008;Francisquini, Rosset, & Nascimento, 2017;Newman, 2004;Santos, Carvalho, & Nascimento, 2016). Blondel et al (2008) presented the Louvain method, a multilevel greedy algorithm for modularity maximization that was successfully applied to a web graph with 118 million vertices and 1 billion edges.…”

Section: Community Detection Algorithmsmentioning

confidence: 99%

An analysis of community structure in Brazilian political topic-based Twitter networks

Tautenhain,

Francisquini,

Nascimento

2019

Preprint

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Online social networks such as Twitter are important platforms for spreading public opinion on a variety of subjects. The classification of users through the analysis of their posts on Twitter according to their opinion sharing can help marketing ads and political campaigns to focus on specific user groups. Community detection-based techniques are specially useful to classify Twitter users, as they do not require rule-based methods or labeled data to perform the clustering task. In this paper, we constructed networks using data related to political discussions in Brazil extracted from Twitter. We show that (i) these networks follow the power-law distribution, indicating that a few popular users are responsible for most of the "mentions" and "retweets"; (ii) the most popular tweets are viral and spread across the communities whereas most of the remaining tweets are trapped in the communities where they originated; and (iii) words associated with positive sentiments are predominant in network communities related to the Brazilian presidential elections and appear in viral tweets.

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GA-LP: A genetic algorithm based on Label Propagation to detect communities in directed networks

Cited by 21 publications

References 23 publications

Weighted label propagation based on Local Edge Betweenness

Weighted label propagation based on Local Edge Betweenness

A Multi-Objective Community Detection Algorithm for Directed Network Based on Random Walk

An analysis of community structure in Brazilian political topic-based Twitter networks

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