LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Li, Huan; Zhang, Ruisheng; Zhao, Zhili; Liu, Xin

doi:10.3390/e23050497

Cited by 31 publications

(12 citation statements)

References 68 publications

(79 reference statements)

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“…However, due to the randomness of it, the results of each run are not the same. Therefore, Huan et al proposed an improved LPA method to improve the performance of community detection [38].…”

Section: Initialization Methods For the CDmentioning

confidence: 99%

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Kang

Wang

et al. 2022

Mathematics

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Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

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Section: Initialization Methods For the CDmentioning

confidence: 99%

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Kang

Wang

et al. 2022

Mathematics

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“…Raghavan et al [28] proposed Label Propagation Algorithm (LPA) which assigns community based on the majority votes by neighbourhood. In recent years, randomness and instability problem in LPA was improved with modularity function [29] and k−core model [30] respectively. All of the above methods were shown to be effective, however these are not easily portable for the streaming graphs.…”

Section: A Non-streamingmentioning

confidence: 99%

Communities in Streaming Graphs: Small Space Data Structure, Benchmark Data Generation, and Linear Algorithm

Gupta¹,

Kundu²

2023

Preprint

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<p>Identifying and preserving community structures in a streaming graph is a very challenging task. However, many applications require the identification of these communities in very limited space and time. In this paper, we design Community Sketch, a small space data structure that efficiently preserves communities. On query, it provides communities in constant time. With the use of community sketch data structure, a linear streaming community detection algorithm is proposed. Experimental results on the large real-world networks show that our algorithm outperforms other state-of-the-art algorithms in terms of quality metrics (NMI, F1-score, and WCC). Further, we propose an algorithm to produce benchmark network, namely, Temporal Community Benchmark Dataset (TCBD) which contains both true community labels and temporal information of edges. These synthetic networks are used to validate the proposed algorithm<br> </p>

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“…This process is repeated until the network reaches a stable state and nodes with the same class are considered to belong to the same community. [115].…”

Section: Plos Onementioning

confidence: 99%

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

et al. 2022

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Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

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LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Cited by 31 publications

References 68 publications

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Communities in Streaming Graphs: Small Space Data Structure, Benchmark Data Generation, and Linear Algorithm

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

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