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Xin Liu
Researcher at National Institute of Advanced Industrial Science and Technology
Publications - 78
Citations - 1135
Xin Liu is an academic researcher from National Institute of Advanced Industrial Science and Technology. The author has contributed to research in topics: Computer science & Modularity (networks). The author has an hindex of 13, co-authored 59 publications receiving 772 citations. Previous affiliations of Xin Liu include Wuhan University of Technology & Wuhan University.
Papers
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Journal ArticleDOI
Advanced modularity-specialized label propagation algorithm for detecting communities in networks
Xin Liu,Tsuyoshi Murata +1 more
TL;DR: Experiments show that LPAm+ successfully detects communities with higher modularity values than ever reported in two commonly used real-world networks and offers a fair compromise between accuracy and speed.
Proceedings ArticleDOI
Community Detection in Large-Scale Bipartite Networks
Xin Liu,Tsuyoshi Murata +1 more
TL;DR: This paper proposes a fast algorithm called LP&BRIM, based on a joint strategy of two developed algorithms -- label propagation (LP), a very fast community detection algorithm, and BRIM, an algorithm for generating better community structure by recursively inducing divisions between the two types of nodes in bipartite networks.
Book ChapterDOI
Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering
TL;DR: This paper presents an approach for the problem of community detection using genetic algorithm (GA) in conjunction with the method of clustering, and demonstrates that the algorithms are highly effective at discovering community structure in both computer-generated and real-world network data.
Proceedings ArticleDOI
A General View for Network Embedding as Matrix Factorization
TL;DR: Experiments show that Matrix factorization based on a new proposed similarity measure and β-tuning strategy significantly outperforms existing matrix factorization approaches on a range of benchmark networks.
Journal ArticleDOI
An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation
TL;DR: Wang et al. as mentioned in this paper proposed an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE), which can learn the lowdimensional representations of informative trajectory images.