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17th International Conference on Web Information Systems Engineering (WISE)

作者： Mera Caraballo, Alexander Arturo Nunes, Bernardo Pereira Lopes, Giseli Rabello Paes Leme, Luiz Andre Portes Casanova, Marco Antonio Pontifical Catholic Univ Rio de Janeiro Dept Informat Rio De Janeiro RJ Brazil Univ Fed Rio de Janeiro Rio De Janeiro RJ Brazil Univ Fed Fluminense Niteroi RJ Brazil Fed Univ State Rio de Janeiro Rio De Janeiro RJ Brazil

ISBN: (纸本)9783319487403;9783319487397

Datasets published on the Web and following the Linked Open Data (LOD) practices have the potential to enrich other LOD datasets in multiple domains. However, the lack of descriptive information, combined with the large number of available LOD datasets, inhibits their interlinking and consumption. Aiming at facilitating such tasks, this paper proposes an automated clustering process for the LOD datasets that, thereby, provide an up-to-date description of the LOD cloud. The process combines metadata inspection and extraction strategies, community detection methods and dataset profiling techniques. The clustering process is evaluated using the LOD diagram as ground truth. The results show the ability of the proposed process to replicate the LOD diagram and to identify new LOD dataset clusters. Finally, experiments conducted by LOD experts indicate that the clustering process generates dataset clusters that tend to be more descriptive than those manually defined in the LOD diagram.

关键词： Linked data cloud analysis Automatic clustering Domain identification community detection algorithms

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community detection using local neighborhood in complex networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 436卷 665-677页

作者： Eustace, Justine Wang, Xingyuan Cui, Yaozu Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China

It is common to characterize community structure in complex networks using local neighborhood. Existing related methods fail to estimate the accurate number of nodes present in each community in the network. In this paper a community detection algorithm using local community neighborhood ratio function is proposed. The proposed algorithm predicts vertex association to a specific community using visited node overlapped neighbors. In the beginning, the algorithm detects local communities;then through iterations and local neighborhood ratio function, final communities are detected by merging close related local communities. Analysis of simulation results on real and artificial networks shows the proposed algorithm detects well defined communities in both networks by wide margin. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Complex networks Data mining community detection algorithms Neighborhood ratio Behavior science

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Unwinding the hairball graph: Pruning algorithms for weighted complex networks

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Physical Review E 2016年第1期93卷 012304-012304页

作者： Navid Dianati []The Lazer Lab Northeastern University Boston Massachusetts 02115 USA and Institute for Quantitative Social Sciences Harvard University Cambridge Massachusetts 02138 USA

Empirical networks of weighted dyadic relations often contain “noisy” edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges. Here, we focus on integer-weighted graphs commonly arising when weights count the occurrences of an “event” relating the nodes. We introduce a simple and intuitive null model related to the configuration model of network generation and derive two significance filters from it: the marginal likelihood filter (MLF) and the global likelihood filter (GLF). The former is a fast algorithm assigning a significance score to each edge based on the marginal distribution of edge weights, whereas the latter is an ensemble approach which takes into account the correlations among edges. We apply these filters to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph. Furthermore, compared with thresholding based on edge weight, we show that our filters extract a larger and significantly sparser giant component.

关键词： Complex systems Transportation networks community detection algorithms

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Electron bunch profile reconstruction based on phase-constrained iterative algorithm

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Physical Review Accelerators and Beams 2016年第3期19卷 032801-032801页

作者： F. Bakkali Taheri I. V. Konoplev G. Doucas P. Baddoo R. Bartolini J. Cowley S. M. Hooker John Adams Institute Department of Physics University of Oxford Denys Wilkinson Building Keble Road Oxford OX1 3RH United Kingdom

The phase retrieval problem occurs in a number of areas in physics and is the subject of continuing investigation. The one-dimensional case, e.g., the reconstruction of the temporal profile of a charged particle bunch, is particularly challenging and important for particle accelerators. Accurate knowledge of the longitudinal (time) profile of the bunch is important in the context of linear colliders, wakefield accelerators and for the next generation of light sources, including x-ray SASE FELs. Frequently applied methods, e.g., minimal phase retrieval or other iterative algorithms, are reliable if the Blaschke phase contribution is negligible. This, however, is neither known a priori nor can it be assumed to apply to an arbitrary bunch profile. We present a novel approach which gives reproducible, most-probable and stable reconstructions for bunch profiles (both artificial and experimental) that would otherwise remain unresolved by the existing techniques.

关键词： community detection algorithms

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Dynamic clustering in Sparse MANETs

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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

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Improving In-Vehicle Network Architectures Using Automated Partitioning algorithms

Improving In-Vehicle Network Architectures Using Automated P...

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IEEE Vehicular Networking Conference (VNC)

作者： Nowdehi, Nasser Kleberger, Pierre Olovsson, Tomas Chalmers Univ Technol Dept Comp Sci & Engn SE-41296 Gothenburg Sweden Volvo Car Corp SE-40531 Gothenburg Sweden

ISBN: (纸本)9781467394116

Today's in-vehicle networks are divided into domains using "best engineering practice". However, as far as we are aware of, there are no existing tools that do this domain partitioning in an automated and optimal way. A strategy for designing in-vehicle networks is to group Electronic Control Units (ECUs) into domains so that each domain isolates a certain functionality and minimizes dependencies to other domains. In this paper, we use an automated partitioning algorithm and apply it to an in-vehicle network from a real, modern car, and we analyze the results from such an approach and compare it with the EVITA reference architecture. Different partitioning criteria can be used, and we investigate security domains based on both message types and on domains optimized to minimize inter-domain traffic. We show that our approach is very flexible and can identify meaningful in-vehicle network domains which are better than the EVITA domains with respect to communication, safety and security. We have also investigated the relationship between safety and security to see if security domains contradict or support partitions based on ASILs.

关键词： in-vehicle network architecture safety security community detection algorithms

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Mapping cortical functions with a local community detection algorithm

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JOURNAL OF COMPLEX NETWORKS 2014年第4期2卷 637-653页

作者： Gronchi, Giorgio Guazzini, Andrea Massaro, Emanuele Bagnoli, Franco Univ Florence Dept Hlth Sci Florence Italy Univ Florence Dept Educ & Psychol Florence Italy Univ Florence CSDC Florence Italy Univ Florence Dept Informat Engn Florence Italy Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA US Army Corps Engineer Risk & Decis Sci Team Concord MA 01742 USA Univ Florence Dept Phys & Astron Florence Italy Ist Nazl Fis Nucl Sez Firenze Florence Italy

With the availability of detailed information about the connectivity among brain areas, network analysis tools have been applied to datasets comprising cat and macaque cortical networks. Using evolutionary optimization algorithms (based on global analysis of the network), similarities between anatomical connections and functional properties of the areas of the brain have been found in previous works. To find functional properties of the brain, we have applied NetExplorer, a novel community detection algorithm based on local information dynamics, to two different datasets of cortical connectivity. We have compared the performances of NetExplorer with the results obtained by other six algorithms (Infomap, Hierarchical Infomap, Louvain, Modularity Optimization, Label Propagation and Oslom) with the same two datasets of cortical connectivity. In both datasets, NetExplorer places among the best algorithms in predicting the actual functional cortical clusters. We discuss the algorithm results considering the misattributions of brain areas to the different clusters and emphasizing the connections that are explainable by their multimodal functions.

关键词： graph theory community detection algorithms corticocortical connections

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community detection algorithms: A comparative analysis

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Physical Review E 2009年第5期80卷 056117-056117页

作者： Andrea Lancichinetti Santo Fortunato Complex Networks and Systems Institute for Scientific Interchange (ISI) Viale S. Severo 65 10133 Torino Italy Physics Department Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy

Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman [Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)] and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom [Proc. Natl. Acad. Sci. U.S.A. 104, 7327 (2007); Proc. Natl. Acad. Sci. U.S.A. 105, 1118 (2008)], Blondel et al. [J. Stat. Mech.: Theory Exp. (2008), P10008], and Ronhovde and Nussinov [Phys. Rev. E 80, 016109 (2009)] have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.

关键词： community detection algorithms comparative analysis understanding community structure community scale complexity classes Complex systems detection algorithms United States BENCHMARKS

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Evaluating cerebral cortex connectivity with local information algorithm

Evaluating cerebral cortex connectivity with local informati...

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9th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Gronchi, Giorgio Guazzini, Andrea Bagnoli, Franco Massaro, Emanuele Univ Florence Dept Hlth Sci I-50121 Florence Italy Univ Florence Dept Educ & Psychol I-50121 Florence Italy Univ Florence Ist Nazl Fis Nucl Dept Phys & Astron I-50121 Florence Italy Univ Florence Dept Informat Engn I-50121 Florence Italy Univ Florence Ctr Study Complex Syst I-50121 Florence Italy

ISBN: (纸本)9781479932115

Previous works have analyzed the cluster organization of the cat cortical network using both traditional multidimensional scaling methods and evolutionary optimization algorithms. Interestingly, the evolutionary optimization principle of previous works is based on the modularity measure used to find communities in network with global algorithms. In this paper, we deepen this point taking into account different community-detection algorithms. We compare the performances of NetExplorer, a local information dynamics algorithm for detecting communities in networks, with six well-known community detection algorithms: Infomap, Hierarchical Infomap, Louvain, Modularity Optimization, Label Propagation and Oslom. The results indicate that NetExplorer is able to detect the four functional clusters where misattributions of some areas are explained by their multimodal function. Results are discussed in terms of misattributions of brain areas to the different clusters emphasizing connections which are explainable (or not) by a cognitive point of view.

关键词： graph theory community detection algorithms corticocortical connections

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A study of research topic visualization using network structure analysis

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Journal of Educational Media and Library Science 2013年第4期50卷 565-596页

作者： Lin, Sung-Chien Department of Information and Communications Shih Hsin University Taipei Taiwan

The Pathfinder network (PFNet) method and the community detection algorithms both are methods which have been widely applied to visual presentation and analysis of research topics. The PFNet method can delete a large amount of insignificant links in networks but also retains the structural characteristics of the original networks, while the community detection algorithms are able to partition networks into a set of cohesive subgroups. However, each of the methods has its deficiencies. The PFNet method cannot automatically find out critical subgroups in input networks and the community detection algorithms do not guarantee nodes in the same subgroup able to be mapped in neighboring area. The integration of these two methods provides a way to alleviate the above problems: The output network from the PFNet method is partitioned using community detection algorithm. In addition, this study also suggests that the use of high frequency terms within papers in subgroups as the labels of research topics to make the analysis and interpretation of results easier. This study takes the field of Information Communication as an analytic case to study the application of the integrated methods, and the data of master theses of the related graduated schools are collected to be used for the analysis. The results show that it is effective to integrate the PFNet method and the community detection algorithms to discover subgroups representing important research topics from the network which is constructed upon the relations between papers in the examined field. The terms with high occurring frequency in subgroups are very relevant to the problems, method, theorems and technologies in the field of Information Communications and its fundamental disciplines, and therefore, they are suitable as the representatives of research topics.

关键词： Analysis of research topics community detection algorithms Information visualization Path-finder Networks

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