Journal Article10.1016/j.eswa.2023.119775
A novel network core structure extraction algorithm utilized variational autoencoder for community detection
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About: This article is published in Expert Systems With Applications. The article was published on 01 Mar 2023. The article focuses on the topics: Computer science & Autoencoder.
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Citations
A comprehensive review of community detection in graphs
Jiakang Li,Songning Lai,Zhihao Shuai,Yuan Tan,Yifan Jia,Matt Yu,Zichen Song,Xiaokang Peng,Ziyang Xu,Yongxin Ni,Haifeng Qiu,Jiayu Yang,Yutong Liu,Yonggang Lu +13 more
TL;DR: This comprehensive review of community detection in graphs presents various methods, including modularity-based, spectral clustering, probabilistic modeling, and deep learning approaches, and introduces a new method, comparing their performance on datasets with and without ground truth.
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Gesture Recognition Based on EEMD and Cosine Laplacian Eigenmap
TL;DR: Wang et al. as mentioned in this paper proposed a gesture recognition method based on manifold learning, which extracts the features through time-frequency domain and dimension reduction, and used SVM for classification.
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Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity
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TL;DR: LNSSCL is an unsupervised community detection algorithm that incorporates local node similarity into stochastic competitive learning, improving the stability and accuracy of the algorithm and achieving better performance than other algorithms.
1
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TL;DR: A novel portal named P4PC is created that provides computational tools and data sources of piRNAs and circRNAs and users can quickly select proper resources for their research projects by viewing detail information and comments in P4PC.
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References
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
- 08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.