Research on Community Detection in Complex Networks Based on Internode Attraction

Sheng, Jinfang; Liu, Cheng; Chen, Long; Wang, Bin; Zhang, Junkai

doi:10.3390/e22121383

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…Using community discovery algorithms for syndrome mining can fully utilise the association information between nodes without merging symptoms. Through visualisation tools, the aggregation of symptoms can be visually displayed, and the cross symptoms between different syndromes are both nodes shared by the community in the graph [ 30 ]. This method reduces information loss and manual intervention in syndrome mining.…”

Section: Discussionmentioning

confidence: 99%

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm

Pang,

Ding,

Bian

et al. 2023

IET Systems Biology

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We analyzed the symptoms composition of Interstitial Cystitis (IC), the regularity of the evolution of symptoms before and after treatment, and the visualization of the community network, to provide a reference for clinical diagnosis and treatment of Interstitial Cystitis. Based on the outpatient electronic case data of 552 patients with Interstitial Cystitis, we used a complex network community discovery algorithm, directed weighted complex network, and Sankey map to mine the data of the symptoms composition of Interstitial Cystitis, the evolution of symptoms before and after treatment and the visualization of the community network, to analyze the epidemiological characteristics of interstitial cystitis symptoms in the real world. By the community division of the complex network of interstitial cystitis symptoms, We finally obtained three core symptom communities. Among them, symptom community A (bladder‐related symptoms) is the symptom community with the highest proportion of nodes (60.00%) in the complex network of Interstitial Cystitis, symptom community B (non‐bladder‐related symptoms 1) ranks second (32.00%) in a complex network of Interstitial Cystitis, and symptom community C (non‐bladder‐related symptoms 2) has the lowest proportion (8.00%). There is a complex evolutionary relationship between the symptoms of Interstitial Cystitis before and after treatment. Among the single symptoms before and after treatment, the decreased rate of Day frequency is 93.22%, and the reduced urgency rate is 93.07%. The decline rate of Nocturia was 82.33%. From the perspective of different communities, the overall symptoms of symptom community A decreased by 34.39% after treatment, the general symptoms of symptom community B decreased by 35.37%, and the prevalent symptoms of symptom community C decreased by 71.43%. In the case of using diet regulation treatment to treat bladder pain, the cure rate of bladder pain can reach 22.67%. The cure rate of burning in bladders can get 15.38% with Percutaneous Sacral neuromodulation, 96.95% with diet regulation treatment, and 100% with Percutaneous Sacral neuromodulation. When using behavioral physiotherapy to treat bladder pain, 3.57% of the patient's symptoms change to bladder discomfort; 4% of the patient's symptoms change to bladder discomfort when using oral medicine to treat bladder pain.Symptom research methods based on community findings can effectively explore the rule of symptom outcome of Interstitial Cystitis before and after treatment, and the results are highly interpretable by professionals.

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Section: Discussionmentioning

confidence: 99%

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm

Pang,

Ding,

Bian

et al. 2023

IET Systems Biology

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“…Jinfang Sheng et al [5] In 2020 proposed a new community detection method called IACD (Inter-node Attraction Community Detection), which is based on the attraction of internal nodes. The algorithm consists of three main steps: evaluating node importance, selecting pairs of attractive nodes, and dividing the community.…”

Section: Related Workmentioning

confidence: 99%

PCMeans: Community Detection using LocalPagerank, Clustering, and K-Means

Louafi

Titouna

2023

Preprint

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With the rise of social networks, the task of community detection in networks has become increasingly difficult in recent years. In this study, weintroduce a novel approach, named PCMeans, which combines PageRank, hierarchical clustering, and K-means algorithms to tackle this issue.Our technique employs local PageRank to identify the most influential nodes, followed by a hierarchical clustering strategy that determinesthe optimal number of clusters, and ultimately applies K-means learning to swiftly converge to the final community structure. PCMeansis an unsupervised method that is easy to implement, efficient, andsimple, and it addresses three common problems, including the random selection of the initial central node, specification of the number ofclasses K, and slow convergence. Our experimental findings show thatPCMeans can efficiently identify communities and outperforms otherrecent approaches on both real networks and synthetic benchmarks.

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“…Network attraction degree refers to the difficulty for a node to establish connections with other nodes in the process of network evolution. A greater attraction degree denotes that it is easier to establish links with other nodes [44].…”

Section: Calculation Of Mutual Attraction Between Nodesmentioning

confidence: 99%

Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction

Jin¹,

Liu²

2022

Sustainability

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Unstable factors such as international relations, geopolitics, and transportation routes make natural gas trade complex and changeable. Diversified and flexible sources of liquefied natural gas (LNG) can guarantee the energy supply security of natural gas-consuming countries. Therefore, it is very important to find potential natural gas trade links to help the government find potential partners and prepare strategically in advance. In this paper, the global LNG network is taken as the research object. In order to fully consider the importance of nodes and the influence of economic and political factors, the “centrality degree” and “node attraction degree” are added into the link prediction algorithm, and multifactor coupling is carried out. The reliability of the improved algorithm is verified using the area under the curve (AUC) evaluation index, and the prediction results are analyzed. The results are as follows: Trinidad, Russia, Algeria, Nigeria, Angola, and Equatorial Guinea (Eq. Guinea) are more likely to establish new LNG trading relationships with other countries. For all potential trade relationships, potential relations involving the above countries are more likely to be realized within 5 years, while potential relations involving China, India, Japan, and South Korea are more likely to be realized within 2 years. China, India, and South Korea are more likely to import LNG from Algeria, and Taiwan Province is more likely to import LNG from Algeria, Angola, Eq. Guinea, and America. On the basis of the above study, states and governments can give priority to the above countries and regions when dealing with the possible LNG supply crisis.

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Research on Community Detection in Complex Networks Based on Internode Attraction

Cited by 9 publications

References 42 publications

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm

PCMeans: Community Detection using LocalPagerank, Clustering, and K-Means

Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction

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