检索结果-内蒙古大学图书馆

IASTED European Conference on Internet and Multimedia Systems and Applications (EuroIMSA)

作者： Puig-Centelles, Anna Ripolles, Oscar Chover, Miguel Univ Jaume 1 Dept Lenguajes & Sistemas Informat Castellon de La Plana 12071 Spain

ISBN: (纸本)9780889866522

In this paper we present a summary of the state of the art concerning community detection in distributed computer architectures, for instance so-called P2P systems. The main algorithms for the extraction of communities are reviewed. Each set of nodes that constitute a community may also overlap with other communities due to the fact that nodes are shared between them. Thus, the the need to detect such communities has led us to examine the software that exists for their detection.

关键词： P2P networks clustering community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient community detection of network flows for varying Markov times and bipartite networks

引用

Physical Review E 2016年第3期93卷 032309-032309页

作者： Masoumeh Kheirkhahzadeh Andrea Lancichinetti Martin Rosvall Department of IT and Computer Engineering Iran University of Science and Technology Teheran Iran Integrated Science Lab Department of Physics Umeå University SE-901 87 Umeå Sweden

community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be more appropriate. Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better. However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions. To overcome this problem, we introduce a method that takes advantage of the inner workings of the map equation and evades the reconstruction step. This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost. The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.

关键词： Network flow community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Relational flexibility of network elements based on inconsistent community detection

引用

Physical Review E 2019年第2期100卷 022311-022311页

作者： Heetae Kim (김희태) Sang Hoon Lee (이상훈) Department of Industrial Engineering Universidad de Talca Curicó 3341717 Chile Asia Pacific Center for Theoretical Physics Pohang 37673 Korea Department of Liberal Arts Gyeongnam National University of Science and Technology Jinju 52725 Korea

community identification of network components enables us to understand the mesoscale clustering structure of networks. A number of algorithms have been developed to determine the most likely community structures in networks. Such a probabilistic or stochastic nature of this problem can naturally involve the ambiguity in resultant community structures. More specifically, stochastic algorithms can result in different community structures for each realization in principle. In this study, instead of trying to “solve” this community degeneracy problem, we turn the tables by taking the degeneracy as a chance to quantify how strong companionship each node has with other nodes. For that purpose, we define the concept of companionship inconsistency that indicates how inconsistently a node is identified as a member of a community regarding the other nodes. Analyzing model and real networks, we show that companionship inconsistency discloses unique characteristics of nodes, thus we suggest it as a new type of node centrality. In social networks, for example, companionship inconsistency can classify outsider nodes without firm community membership and promiscuous nodes with multiple connections to several communities. In infrastructure networks such as power grids, it can diagnose how the connection structure is evenly balanced in terms of power transmission. Companionship inconsistency, therefore, abstracts individual nodes' intrinsic property on its relationship to a higher-order organization of the network.

关键词： community structure Network structure community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Magnetic eigenmaps for community detection in directed networks

引用

Physical Review E 2017年第2期95卷 022302-022302页

作者： Michaël Fanuel Carlos M. Alaíz Johan A. K. Suykens Department of Electrical Engineering (ESAT) and STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Kasteelpark Arenberg 10 B-3001 Leuven Belgium

Communities in directed networks have often been characterized as regions with a high density of links, or as sets of nodes with certain patterns of connection. Our approach for community detection combines the optimization of a quality function and a spectral clustering of a deformation of the combinatorial Laplacian, the so-called magnetic Laplacian. The eigenfunctions of the magnetic Laplacian, which we call magnetic eigenmaps, incorporate structural information. Hence, using the magnetic eigenmaps, dense communities including directed cycles can be revealed as well as “role” communities in networks with a running flow, usually discovered thanks to mixture models. Furthermore, in the spirit of the Markov stability method, an approach for studying communities at different energy levels in the network is put forward, based on a quantum mechanical system at finite temperature.

关键词： Deterministic networks Directed networks Real world networks community detection algorithms Cycles Diffusion & random walks

来源：评论

学校读者我要写书评

暂无评论

community detection in networks with unequal groups

引用

Physical Review E 2016年第1期93卷 012303-012303页

作者： Pan Zhang Cristopher Moore M. E. J. Newman Santa Fe Institute Santa Fe New Mexico 87501 USA State Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Physics Department and the Center for the Study of Complex Systems University of Michigan Ann Arbor Michigan 48109 USA

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

关键词： Network phase transitions Cavity methods community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis on the selection of number of clusters in community detection

引用

Physical Review E 2018年第2期97卷 022315-022315页

作者： Tatsuro Kawamoto Yoshiyuki Kabashima Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology 2-3-26 Aomi Koto-ku Tokyo Japan Department of Mathematical and Computing Science Tokyo Institute of Technology W8-45 2-12-1 Ookayma Meguro-ku Tokyo Japan

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

关键词： community structure Bayesian methods Block models community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Merge-split Markov chain Monte Carlo for community detection

引用

Physical Review E 2020年第1期102卷 012305-012305页

作者： Tiago P. Peixoto Department of Network and Data Science Central European University H-1051 Budapest Hungary ISI Foundation Via Chisola 5 10126 Torino Italy and Department of Mathematical Sciences University of Bath Claverton Down Bath BA2 7AY United Kingdom

We present a Markov chain Monte Carlo scheme based on merges and splits of groups that is capable of efficiently sampling from the posterior distribution of network partitions, defined according to the stochastic block model (SBM). We demonstrate how schemes based on the move of single nodes between groups systematically fail at correctly sampling from the posterior distribution even on small networks, and how our merge-split approach behaves significantly better, and improves the mixing time of the Markov chain by several orders of magnitude in typical cases. We also show how the scheme can be straightforwardly extended to nested versions of the SBM, yielding asymptotically exact samples of hierarchical network partitions.

关键词： community structure Stochastic inference Block models community detection algorithms Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

Generative model for reciprocity and community detection in networks

引用

Physical Review Research 2021年第2期3卷 023209-023209页

作者： Hadiseh Safdari Martina Contisciani Caterina De Bacco Max Planck Institute for Intelligent Systems Cyber Valley Tuebingen 72076 Germany

We present a probabilistic generative model and efficient algorithm to model reciprocity in directed networks. Unlike other methods that address this problem such as exponential random graphs, it assigns latent variables as community memberships to nodes and a reciprocity parameter to the whole network rather than fitting order statistics. It formalizes the assumption that a directed interaction is more likely to occur if an individual has already observed an interaction towards her. It provides a natural framework for relaxing the common assumption in network generative models of conditional independence between edges, and it can be used to perform inference tasks such as predicting the existence of an edge given the observation of an edge in the reverse direction. Inference is performed using an efficient expectation-maximization algorithm that exploits the sparsity of the network, leading to an efficient and scalable implementation. We illustrate these findings by analyzing synthetic and real data, including social networks, academic citations, and the Erasmus student exchange program. Our method outperforms others in both predicting edges and generating networks that reflect the reciprocity values observed in real data, while at the same time inferring an underlying community structure. We provide an open-source implementation of the code online.

关键词： community structure Patterns in complex systems Topological tools Directed networks Social networks community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient detection of Overlapping Communities Using Asymmetric Triangle Cuts

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2018年第11期30卷 2093-2105页

作者： Rezvani, Mojtaba Liang, Weifa Liu, Chengfei Yu, Jeffrey Xu Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising.

关键词： Overlapping community detection fitness metrics for overlapping communities social networks community detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Dynamic clustering in Sparse MANETs

引用

COMPUTER COMMUNICATIONS 2015年 59卷 84-97页

作者： Drugan, O. V. Plagemann, T. Munthe-Kaas, E. DNV GL AS DNV GL Strateg Res & Innovat N-1322 Hovik Norway Univ Oslo Dept Informat N-0316 Oslo Norway

In dynamic networks like sparse mobile ad hoc networks used during rescue operations, data and service availability and reachability is crucial. Such dynamic networks can be analysed to identify stable clusters as well as nodes with frequent cluster affiliation changes;these structural and behavioural properties can then be utilised for careful data and service placement and ferry selection. Factors that influence the outcome are the clustering algorithms used and how and when they are applied. In this paper, we identify cluster evolution based on similarities between clusters detected by community detection algorithms on consecutive network snapshots. The community detection algorithms are node agnostic in the sense that they do not elect cluster heads, and are non-intrusive, i.e., they extract topology information from the local routing tables without interfering with the routing algorithm nor creating network traffic. We have performed extensive experiments to evaluate how the choice of clustering algorithm affects the evolution and timeline of clusters and the identification of node-cluster affiliations. Additionally, we compare how well the algorithms perform on selecting nodes for data and service placement and ferrying. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Sparse MANETs community detection algorithms Non-intrusive clustering Placement of services Data ferry selection

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：