LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks

Lu, Meilian; Zhang, Zhenglin; Qu, Zhihe; Kang, Yu

doi:10.1109/tkde.2018.2866424

Cited by 89 publications

(40 citation statements)

References 24 publications

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“…On community detection with nodal features, there are generally two types of techniques, model-free methods and generative models. Like the structure based algorithms with some optimality criteria to detect communities such as the modularity based methods [9], [30], [31] and label propagation [32], such model-free methods are proposed to exploit the features including structure mining [33], simulated annealing [34], Joint Community Detection Criterion (JCDC) [35], Semidefinite Programming (SDP) [36], and Covariance Assisted Spectral Clustering (CASC) [37]. Most methods in this category exploit features in the same way without considering the relationship between them and communities.…”

Section: Related Workmentioning

confidence: 99%

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Zare

Hajiabadi

Jalili

2021

IEEE Trans. Knowl. Data Eng.

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Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

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Section: Related Workmentioning

confidence: 99%

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Zare

Hajiabadi

Jalili

2021

IEEE Trans. Knowl. Data Eng.

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“…Significant researches have been carried out in identifying communities, which is helpful for understanding and exploiting networks more effectively [6], [7]. For example, detecting communities in citation networks might find the papers on related topics [8].…”

Section: Introductionmentioning

confidence: 99%

“…However, COPRA still cannot obtain stable communities, since the node order of label updating is also random and COPRA randomly chooses one label in multiple labels with same belonging coefficient of a node to propagate to other nodes. To better discover communities, researchers proposed many label propagation algorithms to handle the instability problem by considering node and label importance (their weight) to decide the updating order [7], [14]- [19].…”

Section: Introductionmentioning

confidence: 99%

“…By analyzing the label propagation process in above methods, we find that there are some limits: 1) the node order of label updating is random, 2) node importance only measures the weight of nodes themselves but does not evaluate their influence to others, 3) a node only accepts the labels from its neighbor nodes and spreads one label to others once although this node may own many labels, 4) the weight of labels is constant when the labels are launched from a node to another node, which may cause the instability problem and lead methods detecting erroneous communities. Some researches [7], [14], [17]- [20] are proposed to solve the first problem to enhance the stability. To our best knowledge, there are few researches to deal with 2) -4) problems.…”

Section: Introductionmentioning

confidence: 99%

“…In order to handle the randomness, we fix the node order of label updating in the ascending order of propagation ability. Different with node importance [7], [15]- [19], the four abilities of nodes can reflect the influence of nodes to others, which include the influence range and the quantity of label launching and accepting. A node can receive labels from farther nodes according to propagation ability and attraction ability and can launch and accept multiple labels according to launch ability and acceptance ability, which is beneficial for detecting accurate communities and converging fast.…”

Section: Introductionmentioning

confidence: 99%

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NALPA: A Node Ability Based Label Propagation Algorithm for Community Detection

et al. 2020

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Community is an important topological characteristic of complex networks, which is significant for understanding the structural feature and organizational function of networks, and community detection has recently attracted considerable research effort. Among community detection methods, label propagation technology is widely used because of its linear time complexity. However, due to the randomness of the node order of label updating and the order of label launching in label propagation, the instability of community detection approaches based on label propagation becomes a challenge. In this paper, a new label propagation algorithm, Node Ability based Label Propagation Algorithm (NALPA), is proposed to discover communities in networks. Inspired from human society and radar transmission, we design four node abilities (propagation ability, attraction ability, launch ability and acceptance ability), label influence and a new label propagation mechanism to deal with the instability and enhance the efficiency. Experimental results on 42 synthetic and 14 real-world networks demonstrate that NALPA outperforms state-of-the-art approaches in most cases. In a case study, NALPA is applied to a drug network in Traditional Chinese Medicine (TCM) and can discover the drug communities where drugs have similar efficacy for treating Chronic GlomeruloNephritis (CGN).

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Label entropy‐based cooperative particle swarm optimization algorithm for dynamic overlapping community detection in complex networks

Jiang

Pan

et al. 2021

Int J of Intelligent Sys

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The real-world complex networks, such as biological, transportation, biomedical, web, and social networks, are usually dynamic and change over time. The communities which reflect the substructures hidden in the networks usually overlap each other, and detecting overlapping communities in the dynamic complex networks is a challenging task. Prior researchers have applied multiobjective optimization method to the detection of dynamic overlapping communities and achieved some excellent results. However, in terms of multiobjective processing, the prior studies all adopt the decomposition method based on weight parameters, and different weight parameters or different parameter values can easily affect the community detection results which further results in the uneven distribution of the detected results in the target space. To solve the above problems, a hybrid algorithm, that is, Collaborative Particle Swarm multiobjective Optimization-based Dynamic Overlapping Community Detection (CPSO-DOCD) algorithm is proposed in this paper. First, to improve the diversity of particles, the

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LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks

Cited by 89 publications

References 24 publications

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

NALPA: A Node Ability Based Label Propagation Algorithm for Community Detection

Label entropy‐based cooperative particle swarm optimization algorithm for dynamic overlapping community detection in complex networks

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