J
Jianxin Tang
Researcher at Lanzhou University
Publications - 10
Citations - 281
Jianxin Tang is an academic researcher from Lanzhou University. The author has contributed to research in topics: Maximization & Metaheuristic. The author has an hindex of 6, co-authored 7 publications receiving 169 citations. Previous affiliations of Jianxin Tang include Lanzhou University of Technology.
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A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection
TL;DR: A novel mutation enhanced BPSO-SVM algorithm is presented by adjusting the memory of local and global optimum (LGO) and increasing the particles’ mutation probability for feature selection to overcome convergence premature problem and achieve high quality features.
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A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks
TL;DR: An effective discrete shuffled frog-leaping algorithm (DSFLA) is proposed to solve influence maximization problem in a more efficient way and is superior than several state-of-the-art alternatives.
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Maximizing the spread of influence via the collective intelligence of discrete bat algorithm
Jianxin Tang,Jianxin Tang,Ruisheng Zhang,Yabing Yao,Zhili Zhao,Ping Wang,Huan Li,Jinliang Yuan +7 more
TL;DR: A metaheuristic discrete bat algorithm based on the collective intelligence of bat population is proposed and it is demonstrated that DBA outperforms other two metaheuristics and the Stop-and-Stair algorithm, and achieves competitive influence spread to CELF but has less time computation than CELF.
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Link prediction in complex networks based on the interactions among paths
TL;DR: A local path-based link predictor which emphasizes the effect of the Resources from Short Paths (RSP) is proposed and experiments demonstrate that the RSP index has better performance than other nine structure-based similarity methods.
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Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization
Jianxin Tang,Jianxin Tang,Ruisheng Zhang,Yabing Yao,Fan Yang,Zhili Zhao,Rongjing Hu,Yongna Yuan +7 more
TL;DR: An improved discrete particle swarm optimization with an enhanced network topology-based strategy for influence maximization that outperforms typical centrality-based heuristics, and achieves comparable results to greedy algorithm but with less time complexity.