Self-adaptive Louvain algorithm: Fast and stable community detection algorithm based on the principle of small probability event

Zhang, Ziqiao; Pu, Peng; Han, Dongdong; Tang, Ming

doi:10.1016/j.physa.2018.04.036

Cited by 31 publications

(9 citation statements)

References 29 publications

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“…Compared with the Louvain-based community detection method, the clustering method proposed in the paper shows more superiorability to mine the effective information with confused functional connection matrices (see Figure 8 , Figure 9 and Figure 11 ). In Louvain algorithm, the community is formed according to the largest modularity increment [ 50 ]. The modularity is defined as the difference between the connection weights of nodes in the community and the connection weights in a random case.…”

Section: Discussionmentioning

confidence: 99%

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

Zhang

Zhao

et al. 2020

Entropy

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The countermeasure of driver fatigue is valuable for reducing the risk of accidents caused by vigilance failure during prolonged driving. Listening to the radio (RADIO) has been proven to be a relatively effective “in-car” countermeasure. However, the connectivity analysis, which can be used to investigate its alerting effect, is subject to the issue of signal mixing. In this study, we propose a novel framework based on clustering and entropy to improve the performance of the connectivity analysis to reveal the effect of RADIO to maintain driver alertness. Regardless of reducing signal mixing, we introduce clustering algorithm to classify the functional connections with their nodes into different categories to mine the effective information of the alerting effect. Differential entropy (DE) is employed to measure the information content in different brain regions after clustering. Compared with the Louvain-based community detection method, the proposed method shows more superior ability to present RADIO effectin confused functional connection matrices. Our experimental results reveal that the active connection clusters distinguished by the proposed method gradually move from frontal region to parieto-occipital regionwith the progress of fatigue, consistent with the alpha energy changes in these two brain areas. The active class of the clusters in parieto-occipital region significantly decreases and the most active clusters remain in the frontal region when RADIO is taken. The estimation results of DE confirm the significant change (p < 0.05) of information content due to the cluster movements. Hence, preventing the movement of the active clusters from frontal region to parieto-occipital region may correlate with maintaining driver alertness. The revelation of alerting effect is helpful for the targeted upgrade of fatigue countermeasures.

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Section: Discussionmentioning

confidence: 99%

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

Zhang

Zhao

et al. 2020

Entropy

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“…If a gain exists in the modularity, l is moved to this new community; otherwise, it remains in its original community. is mechanism is repeated for all nodes in the network [49].…”

Section: Building the Modelmentioning

confidence: 99%

Modeling the Interaction Networks about the Climate Change on Twitter: A Characterization of its Network Structure

López

Subirán

2022

Complexity

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This work studies the interaction networks (replying, retweeting, and quoting) that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. The largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. The quoting and retweeting networks have a more hierarchical structure than the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled (with high accuracy) through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. The cumulative probability distributions of hashtags per community are also examined.

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“…In recent years, the Louvain algorithm and improved algorithms based on it have been widely applied in largescale complex network partition [29][30][31][32][33][34][35][36]. Perrin et al [36] proposed a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks.…”

Section: Louvain Algorithmmentioning

confidence: 99%

An Improved Louvain Algorithm for Community Detection

Zhang

Fei

Song

et al. 2021

Mathematical Problems in Engineering

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Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.

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Self-adaptive Louvain algorithm: Fast and stable community detection algorithm based on the principle of small probability event

Cited by 31 publications

References 29 publications

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

Modeling the Interaction Networks about the Climate Change on Twitter: A Characterization of its Network Structure

An Improved Louvain Algorithm for Community Detection

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