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Seed-Oriented Local Community Detection Based on Influence Spreading

Computer Modeling in Engineering & Sciences 2022年第7期132卷 215-249页

作者： Shenglong Wang Jing Yang Xiaoyu Ding Jianpei Zhang Meng Zhao College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China

In recent years,local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks.However,there are still some issues that need to be further studied.First,there is no local community detection algorithm dedicated to detecting a seed-oriented local community,that is,the local community with the seed as the core.The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters,respectively.To solve the existing problems,we propose a seed-oriented local community detection algorithm,named SOLCD,that is based on influence spreading.First,we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes.Second,we obtain the seed-oriented local community,which is composed of the may-members and the must-member chain of the seed,by detecting the influence scope of the seed.The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed.Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks.The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters.In addition,when taking nodes with different influence as seeds,SOLCD can stably obtain high-quality seed-oriented local communities.

关键词： Complex network local community detection influence spreading seed-oriented degree centrality k-core centrality local expansion

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CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

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Petroleum 2020年第4期6卷 353-361页

作者： Makumbonori Bristone Rajesh Prasad Adamu Ali Abubakar School of IT and Computing American University of NigeriaYolaAdamawa StateNigeria Department of Computer Science and Engineering African University of Science and TechnologyAbujaNigeria

Crude oil price prediction is a challenging task in oil producing countries.Its price is among the most complex and tough to model because fluctuations of price of crude oil are highly irregular,nonlinear and varies dynamically with high uncertainty.This paper proposed a hybrid model for crude oil price prediction that uses the complex network analysis and long short-term memory(LSTM)of the deep learning algorithms.The complex network analysis tool called the visibility graph is used to map the dataset on a network and k-core centrality was employed to extract the non-linearity features of crude oil and reconstruct the dataset.The complex network analysis is carried out in order to preprocess the original data to extract the non-linearity features and to reconstruct the data.Thereafter,LSTM was employed to model the reconstructed data.To verify the result,we compared the empirical results with other research in the literature.The experiments show that the proposed model has higher accuracy,and is more robust and reliable.

关键词： Complex network analysis Deep learning Long-short term memory network k-core centrality Artificial intelligence Crude oil price prediction

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