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2010 Chinese Control and Decision Conference

作者： Zhuoda Jiang Key Laboratory of Numerical Control of Jiangxi Province,Jiujiang University,Jiujiang,Jiangxi,332005,China

＜正＞The improved voting strategy for pairwise classification of multi-class support vector machine（MSVM） is *** new voting strategy can increase recognition accuracy and resolve the unclassifiable region problems caused by conventional pairwise *** improved voting value equals to the traditional voting value plus the tuning *** the data in the classifiable regions,the classification results using improved voting strategy are the same as that using the traditional ***,the data in the unclassifiable region can be determined by the tuning *** computer simulations using four UCI data sets,the superiorities of the presented multi-class strategy are demonstrated.

关键词： Multi-class Support Vector Machine(MSVM) voting strategy Unclassifiable Region Pairwise Classification

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Maximizing Influence in Temporal Social Networks:A Node Feature-Aware voting Algorithm

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Computers, Materials & Continua 2023年第12期77卷 3095-3117页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of ***,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social *** bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation ***,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes ***,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their *** the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability *** process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based ***,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node ***,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关键词： Temporal social networks influence maximization voting strategy interactive properties self-similarity

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