Z
Zhili Zhao
Researcher at Lanzhou University
Publications - 53
Citations - 431
Zhili Zhao is an academic researcher from Lanzhou University. The author has contributed to research in topics: Workflow & Computer science. The author has an hindex of 8, co-authored 46 publications receiving 262 citations. Previous affiliations of Zhili Zhao include Free University of Berlin.
Papers
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Journal ArticleDOI
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.
Book ChapterDOI
Reaction RuleML 1.0: standardized semantic reaction rules
TL;DR: This paper addresses the Reaction RuleML subfamily of RuleML and survey related work, a standardized rule markup/serialization language and semantic interchange format for reaction rules and rule-based event processing.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China
TL;DR: A hybrid ensemble model CERL is proposed to exploit the merits of both forward neural networks and recurrent neural networks that are designed for handling time serial data to predict air quality hourly to provide better performance over other baseline models.