An Improved Louvain Algorithm for Community Detection

Zhang, Jicun; Fei, Jiyou; Song, Xiangfei; Feng, Jiawei

doi:10.1155/2021/1485592

Cited by 29 publications

(15 citation statements)

References 40 publications

(41 reference statements)

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“…Louvain algorithm [13] is a modularity-based community detection algorithm that can achieve fast clustering of network nodes, especially for networks with lots of nodes but fewer links. And there are lots of community detection methods [14,15] based on Louvain algorithm. But all the above algorithms could only realize the clusters on graph with its structure, leading to rough aggregation of nodes.…”

Section: Clusters and Communitiesmentioning

confidence: 99%

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Understanding User-Level IP Blocks on the Internet

Ren

et al. 2022

Security and Communication Networks

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Generally, the devices on the Internet are identified by IP addresses. The users of IPs are those who use IPs on the Internet and are always different from their registers and operators. Since IPs are used as unique identifiers of devices, knowing users of IPs according to their multisource data is critical for experts to protect the security of the network. At present, there are only few methods to mine the users of IPs from their public data. To make matters worse, the existing methods do not make effective use of a large amount and multisource data, such as certificates, protocol banner, rDNS, location, topology, etc. As a result, the performances of existing methods are largely limited. To tackle this issue, we proposed ULIB, short for “Understanding User-Level IP Blocks on the Internet.” ULIB is based on improved community detection to mine the users for as many blocks of IPs as possible. By analysing comprehensive attributes of IPs, ULIB is able to recognize users effectively. Meanwhile, we evaluated our methodology in the real world and the experiments demonstrated that the accuracy of ULIB is 74.20% and the coverage is 28.90% in a city of China, which outperforms other existing methods.

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Section: Clusters and Communitiesmentioning

confidence: 99%

“…Modularity [14] is a commonly used property to measure the division of network communities. e value of modularity mainly depends on the distribution of nodes in the communities of the network, namely, the community division of the network.…”

Section: Modularity Modelmentioning

confidence: 99%

Understanding User-Level IP Blocks on the Internet

Ren

et al. 2022

Security and Communication Networks

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“…Initialization of GCDNMF. Since the NMF-based approach is sensitive to random initial values of the variables, to overcome this problem, the node membership matrix H of our model is initialized based on our previously presented work K-rank-D [38], which utilizes the centrality and dispersion of the network to determine the cluster centers.…”

Section: Joint Community Detection Modelmentioning

confidence: 99%

“…To formulate the centrality of nodes in the network, according to the algorithm in [38], by modifying the transition probability matrix P, where P ij = A ij /∑ j A ij , we get the centrality of nodes by:…”

Section: Joint Community Detection Modelmentioning

confidence: 99%

General Community Detection in Attributed Networks with Consistent-Module Constrained Nonnegative Matrix Factorization

Liu

Wei

et al. 2022

Wireless Communications and Mobile Computing

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Nonnegative matrix factorization (NMF) model has been successfully applied to discover latent community structures due to its good performance and interpretability advantages in extracting hidden patterns. However, most previous studies explore only the structural information of the network while ignoring the rich attributes. Besides, they aim at detecting densely connected communities (also called community structures) and fail to identify general structures, such as bipartite structures and mixture structures. In this paper, we research on general structure discovery and propose a new method GCDNMF (General Community Detection based on Nonnegative Matrix Factorization), which integrates structural information and node attributes through consistency module constraint to capture the community interactions. It can discover the general community structures of nodes by iteratively updating the community-interaction matrix and the node-membership matrix. We also introduce matrix initialization based on centrality and dispersion of nodes for center selection to reduce the sensitivity of random initialization. Experimental results on real-world networks with a variety of characteristics validate the performance of our approach, especially on networks with general structures. In addition, the associated initialization evaluations demonstrate the effectiveness of our method in obtaining stable results.

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“…proposed an improved Fast Louvain algorithm based on the traditional Louvain algorithm, which is superior to the traditional method in effect and efficiency [5]. Some scholars use big data technology to mine the characteristics of network public opinion.…”

Section: Literature Review 21 Research On Internet Public Opinionmentioning

confidence: 99%

Research on COVID-19 network public opinion events based on topic model and community detection algorithm

Huang

Wang

Yan

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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COVID-19 has caused a large number of online public opinion incidents. How to timely and effectively guide the resulting network public opinion has become an urgent problem to be solved. This paper collects more than 130,000 original Weibo posts during the Wuhan “city closure” incident, and analyses the topic characteristics of the incident on the basis of user classification through topic models and community detection algorithms. It was found that during this period, the government responded quickly to the epidemic and gained public support. For different Weibo users, officially certified users mainly publish information about the epidemic and epidemic prevention measures. Personally certified users mainly forwarded and transmitted official information actively, and they also expressed their opinions and made suggestions. Non-certified users actively expressed their emotions and opinions, so they were important users that reflect public opinion.

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An Improved Louvain Algorithm for Community Detection

Cited by 29 publications

References 40 publications

Understanding User-Level IP Blocks on the Internet

Understanding User-Level IP Blocks on the Internet

General Community Detection in Attributed Networks with Consistent-Module Constrained Nonnegative Matrix Factorization

Research on COVID-19 network public opinion events based on topic model and community detection algorithm

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