检索结果-内蒙古大学图书馆

Fast markov clustering algorithm Based on Belief Dynamics

IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3716-3725页

作者： Li, Huijia Xu, Wenzhe Qiu, Chenyang Pei, Jian Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Nanjing Univ Finance & Econ Jiangsu Prov Key Lab Business Nanjing 210003 Peoples R China

Graph clustering is one of the most significant, challenging, and valuable topic in the analysis of real complex networks. To detect the cluster configuration accurately and efficiently, we propose a new markov clustering algorithm based on the limit state of the belief dynamics model. First, we present a new belief dynamics model, which focuses beliefs of multicontent and randomly broadcasting information. A strict proof is provided for the convergence of nodes' normalized beliefs in complex networks. Second, we introduce a new markov clustering algorithm (denoted as BMCL) by employing a belief dynamics model, which guarantees the ideal cluster configuration. Following the trajectory of the belief convergence, each node is mapped into the corresponding cluster repeatedly. The proposed BMCL algorithm is highly efficient: the convergence speed of the proposed algorithm researches O(TN) in sparse networks. Last, we implement several experiments to evaluate the performance of the proposed methods.

关键词： Heuristic algorithms clustering algorithms Convergence markov processes Broadcasting Computational complexity Trajectory Belief dynamics complex networks convergence large-scale networks markov clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Evolutionary markov Dynamics for Network Community Detection

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第3期34卷 1206-1220页

作者： Wang, Zhen Wang, Chunyu Li, Xianghua Gao, Chao Li, Xuelong Zhu, Junyou Northwestern Polytech Univ Ctr OPTIMAL Xian 710072 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

Community structure division is a crucial problem in the field of network data analysis. algorithms based on markov chains are easy to use and provide promising solutions for community detection. In a markov chain-based algorithm (i.e., MCL), a flow distribution matrix and a transition matrix are used to describe stochastic flows and transition probabilities, respectively, on a network. The dynamic interaction process between stochastic flows and transition probabilities in MCLs is manifested through an iterative process of updating the abovementioned two matrices. As one of the key mechanisms of MCLs, such a dynamic process for increasing the inhomogeneity directly affects the accuracy and computational cost of MCL-based methods. Inspired by a kind of positive feedback interaction of a dendritic network of tube-like amoeba cell pseudopodia (named the Physarum foraging network), a Physarum-inspired relationship among vertices is proposed to enhance the transition probability in the dynamic process of MCL-based community detection algorithms. Specifically, the proposed hybrid community detection algorithm can adaptively search for a better combination of parameters based on a genetic algorithm. Some experiments are carried out on both static and dynamic networks. The results show that the unique Physarum inspired algorithm achieved better computational efficiency and detection performance than other algorithms.

关键词： Heuristic algorithms clustering algorithms markov processes Measurement Detection algorithms Mathematical model Genetic algorithms Complex networks community detection physarum markov clustering algorithm genetic algorithm dynamic networks

来源：评论

学校读者我要写书评

暂无评论

Wind Direction Division of Wind Farm Based on Spontaneous Aggregation Characteristics of Wind-direction Data 38

Wind Direction Division of Wind Farm Based on Spontaneous Ag...

引用

38th Chinese Control Conference (CCC)

作者： Xi, Yunhua Pan, Chenyang Hu, Yang North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (纸本)9789881563972

Wind direction information is of great significance to both wind energy assessment and wind power characteristic analysis. How to divide the wind direction sectors, while taking into account the continuous random fluctuation of the wind direction in the time and space dimension, has become a hot topic worthy of study. This paper presents a wind sector division method considering spontaneous aggregation characteristics of wind-direction measured data. Firstly, the basic knowledge about markov clustering (MCL) algorithm used in wind direction division is introduced. Secondly, the relative variations of wind direction are calculated for the periodic wind direction data, and its probability distribution is estimated statistically. The uniform division interval for the discrete states of markov chain is then determined according to the cumulative probability quantile boundary of the relative wind direction variations. Then, the discrete states of wind direction are regarded as the nodes of a directional graph. And the graph using state transition matrix as the connection weight is clustered by the MCL algorithm. The evaluation indexes are also defined to judge the clustering performance. Finally, the actual data of the wind farm is adopted to verify the effectiveness of clustering results. The simulation results show that using MCL algorithm can fully utilize spontaneous aggregation characteristics of the wind direction to obtain effective wind-direction division results.

关键词： Spontaneous aggregation wind direction wind direction division markov clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Network Community Detection Based on the Physarum-lnspired Computational Framework

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第6期15卷 1916-1928页

作者： Gao, Chao Liang, Mingxin Li, Xianghua Zhang, Zili Wang, Zhen Zhou, Zhili Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Southwest Univ Coll Software Chongqing 400715 Peoples R China Deakin Univ Sch Informat Technol Locked Bag 20000 Geelong Vic 3200 Australia Northwestern Polytech Univ Xian 710072 Shaanxi Peoples R China Nanjing Univ Informat Sci & Technol Coll Comp Sci Nanjing 210044 Jiangsu Peoples R China

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, a kind of slime, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost.

关键词： Community detection Physarum genetic algorithm ant colony optimization markov clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Wind Direction Division of Wind Farm Based on Spontaneous Aggregation Characteristics of Wind-direction Data

Wind Direction Division of Wind Farm Based on Spontaneous Ag...

引用

第三十八届中国控制会议

作者： Yunhua Xi Chenyang Pan Yang Hu School of Control and Computer Engineering North China Electric Power University

Wind direction information is of great significance to both wind energy assessment and wind power characteristic analysis. How to divide the wind direction sectors, while taking into account the continuous random fluctuation of the wind direction in the time and space dimension, has become a hot topic worthy of study. This paper presents a wind sector division method considering spontaneous aggregation characteristics of wind-direction measured data. Firstly, the basic knowledge about markov clustering(MCL) algorithm used in wind direction division is introduced. Secondly, the relative variations of wind direction are calculated for the periodic wind direction data, and its probability distribution is estimated statistically. The uniform division interval for the discrete states of markov chain is then determined according to the cumulative probability quantile boundary of the relative wind direction variations. Then, the discrete states of wind direction are regarded as the nodes of a directional graph. And the graph using state transition matrix as the connection weight is clustered by the MCL algorithm. The evaluation indexes are also defined to judge the clustering performance. Finally, the actual data of the wind farm is adopted to verify the effectiveness of clustering results. The simulation results show that using MCL algorithm can fully utilize spontaneous aggregation characteristics of the wind direction to obtain effective wind-direction division results.

关键词： Spontaneous aggregation wind direction wind direction division markov clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A soft computing approach for benign and malicious web robot detection

引用

EXPERT SYSTEMS WITH APPLICATIONS 2017年 87卷 129-140页

作者： Zabihimayvan, Mandieh Sadeghi, Reza Rude, H. Nathan Doran, Derek Wright State Univ Dept Comp Sci & Engn Kno E Sis Res Ctr Dayton OH 45435 USA

The accurate detection of web robot sessions from a web server log is essential to take accurate traffic level measurements and to protect the performance and privacy of information on a Web server. Moreover, the irrecoverable risks of visits from malicious robots that intentionally try to evade web server intrusion detection systems, covering-up their visits with fabricated fields in their http request packets, cannot be ignored. To separate both types of robots from humans in practice, analysts turn to heuristic methods or state-of-the-art soft computing approaches that have only been tuned to the specification of a kind of web server. Noting that the landscape of web robot agents is ever changing, and that behavioral patterns and characteristics vary across different web servers, both options are lacking. To overcome this challenge, this paper presents SMART, a soft computing system that simultaneously detects benign and malicious types of robot agents from web server logs and can automatically adapt to the session characteristics of a web server. The results of experiments over some access log file servers, each servicing different domains of the web, demonstrate outperformance of the proposed method on state-of-the-art ones for benign and malicious robot detection. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： markov clustering algorithm Web Robot Detection Web crawler Malicious web agents Fuzzy Rough Set Theory

来源：评论

学校读者我要写书评

暂无评论

Building ontology from texts

Building ontology from texts

引用

International Conference on Advanced Wireless Information and Communication Technologies (AWICT)

作者： Benaissa, Bedr-eddine Bouchiha, Djelloul Zouaoui, Amine Doumi, Noureddine Ctr Univ Naama Dept Math & Comp Sci El Bayad Algeria Univ Djillali Liabes Sidi Bel Abbes Dept Math & Comp Sci Sidi Bel Abbes Algeria Univ Dr Tahar Moulay Saida Saida Algeria

The purpose of this paper is to present an approach to create semi-automatically ontology from Arabic texts. The whole process is supervised by a linguistic expert. Our involvement in this project focused on a lexical ontology, taking as model the WordNet ontology, and as input source, the "Arabic verbs" of a contemporary monolingual dictionary mcjm Al gamma ny/in the form a lexical database. The verb, pivot of a sentence, is our goal in creating concepts, by adopting the synset as our meaning representation model. The markov clustering algorithm of a graph, generated by the defining verbs, obtained from the transitive closure, allowed us to detect similar verbs and to identify as well, for a given verbal entry, all of its synonyms. A tool has been implemented, and experiments have been carried out to evaluate and show efficiency of the proposed approach. (C) 2015 Published by Elsevier B. V.

关键词： Ontology Learning (Lexical) Ontology Arabic Texts NLP markov clustering algorithm WordNet

来源：评论

学校读者我要写书评

暂无评论

Building Ontology from Texts

引用

Procedia Computer Science 2015年 73卷 7-15页

作者： Bedr-eddine Benaissa Djelloul Bouchiha Amine Zouaoui Noureddine Doumi Department Mathematics and Computer Science Ctr Univ Naama Algeria Department Mathematics and Computer Science University of Sidi Bel-Abbes Algeria University Dr. Tahar Moulay of Saida Algeria

The purpose of this paper is to present an approach to create semi-automatically ontology from Arabic texts. The whole process is supervised by a linguistic expert. Our involvement in this project focused on a lexical ontology, taking as model the WordNet ontology, and as input source, the “Arabic verbs” of a contemporary monolingual dictionary ( ) /mζjm Alγny/ in the form a lexical database. The verb, pivot of a sentence, is our goal in creating concepts, by adopting the synset as our meaning representation model. The markov clustering algorithm of a graph, generated by the defining verbs, obtained from the transitive closure, allowed us to detect similar verbs and to identify as well, for a given verbal entry, all of its synonyms. A tool has been implemented, and experiments have been carried out to evaluate and show efficiency of the proposed approach.

关键词： Ontology Learning (Lexical) Ontology Arabic Texts NLP markov clustering algorithm WordNet.

来源：评论

学校读者我要写书评

暂无评论

Characterization and analysis of a transcriptome from the boreal spider crab Hyas araneus

引用

COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS 2013年第4期8卷 344-351页

作者： Harms, Lars Frickenhaus, Stephan Schiffer, Melanie Mark, Felix C. Storch, Daniela Poertner, Hans-Otto Held, Christoph Lucassen, Magnus Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Bremerhaven Germany Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Bremerhaven Germany Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Bremerhaven Germany

Research investigating the genetic basis of physiological responses has significantly broadened our understanding of the mechanisms underlying organismic response to environmental change. However, genomic data are currently available for few taxa only, thus excluding physiological model species from this approach. In this study we report the transcriptome of the model organism Hyas araneus from Spitsbergen (Arctic). We generated 20,479 transcripts, using the 454 GS FIX sequencing technology in combination with an Illumina HiSeq sequencing approach. Annotation by Blastx revealed 7159 blast hits in the NCBI non-redundant protein database. The comparison between the spider crab H. araneus transcriptome and EST libraries of the European lobster Homarus americanus and the porcelain crab Petrolisthes cinctipes yielded 3229/2581 sequences with a significant hit, respectively. The clustering by the markov clustering algorithm (MCL) revealed a common core of 1710 clusters present in all three species and 5903 unique clusters for H. araneus. The combined sequencing approaches generated transcripts that will greatly expand the limited genomic data available for crustaceans. We introduce the MCL clustering for transcriptome comparisons as a simple approach to estimate similarities between transcriptomic libraries of different size and quality and to analyze homologies within the selected group of species. In particular, we identified a large variety of reverse transcriptase (RT) sequences not only in the H. araneus transcriptome and other decapod crustaceans, but also sea urchin, supporting the hypothesis of a heritable, anti-viral immunity and the proposed viral fragment integration by host-derived RTs in marine invertebrates. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Spider crab (Hyas araneus) Crustacea Transcriptome markov clustering algorithm Anti-viral immunity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：