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community Analysis of a Crisis Response Network

SOCIAL SCIENCE COMPUTER REVIEW 2021年第3期39卷 371-390页

作者： Kim, Yushim Kim, Jihong Oh, Seong Soo Kim, Sang-Wook Ku, Minyoung Cha, Jaehyuk Arizona State Univ Sch Publ Affairs Phoenix AZ USA Hanyang Univ Dept Comp Sci & Engn Seoul South Korea Hanyang Univ 222 Wangshimni Ro Seoul 04763 South Korea Hanyang Univ Dept Comp & Software Seoul South Korea John Jay Coll New York NY USA

This article distinguishes between clique family subgroups and communities in a crisis response network. Then, we examine the way organizations interacted to achieve a common goal by employing community analysis of an epidemic response network in Korea in 2015. The results indicate that the network split into two groups: core response communities in one group and supportive functional communities in the other. The core response communities include organizations across government jurisdictions, sectors, and geographic locations. Other communities are confined geographically, homogenous functionally, or both. We also find that whenever intergovernmental relations were present in communities, the member connectivity was low, even if intersectoral relations appeared together within them.

关键词： epidemic response network community detection algorithms network analysis emergency management

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An efficient method for mining the maximal alpha-quasi-clique-community of a given node in complex networks

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SOCIAL NETWORK ANALYSIS AND MINING 2018年第1期8卷 1-18页

作者： Conde-Cespedes, Patricia Ngonmang, Blaise Viennet, Emmanuel ISEP LISITE 10 Rue Vanves F-92130 Issy Les Moulineaux France Univ Paris 13 L2TI 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France

Detecting communities in large complex networks is important to understand their structure and to extract features useful for visualization or prediction of various phenomena like the diffusion of information or the dynamic of the network. A community is defined by a set of strongly interconnected nodes. An alpha-quasi-clique is a group of nodes where each member is connected to more than a proportion alpha of the other nodes. By construction, an alpha-quasi-clique has a density greater than alpha. The size of an alpha-quasi-clique is limited by the degree of its nodes. In complex networks whose degree distribution follows a power law, usually alpha-quasi-cliques are small sets of nodes for high values of alpha. In this paper, we present an efficient method for finding the maximal alpha-quasi-clique of a given node in the network. Therefore, the resulting communities of our method have two main characteristics: they are alpha-quasi-cliques (very dense for high alpha) and they are local to the given node. Detecting the local community of specific nodes is very important for applications dealing with huge networks, when iterating through all nodes would be impractical or when the network is not entirely known. The proposed method, called RANK-NUM-NEIGHS (RNN), is evaluated experimentally on real and computer-generated networks in terms of quality (community size), execution time and stability. We also provide an upper bound on the optimal solution.

关键词： community detection algorithms Maximal alpha-quasi-clique Local community detection Complex networks Density

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Semantic Divergence based Evaluation of Web Service Communities

Semantic Divergence based Evaluation of Web Service Communit...

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13th IEEE International Conference on Services Computing (SCC)

作者： Naim, Hafida Aznag, Mustapha Quafafou, Mohamed Durand, Nicolas Aix Marseille Univ CNRS UMR 7296 LSIS F-13397 Marseille France

ISBN: (纸本)9781509026289

The number of community detection algorithms is growing continuously adopting a topological based approach to discover optimal subgraphs or communities. In this paper, we propose a new method combining both topology and semantic to evaluate and rank community detection algorithms. To achieve this goal we consider a probabilistic topic based approach to define a new measure called semantic divergence of communities. Combining this measure with others related to prior knowledge, we compute a score for each algorithm to evaluate the effectiveness of its communities and propose a ranking method. We have evaluated our approach considering communities of real web services.

关键词： community detection algorithms Web services Service communities Topic models Semantic divergence Dependency network

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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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Networks Clustering-Based Approach for Search of Reservoirs-Analogues 22nd

Networks Clustering-Based Approach for Search of Reservoirs-...

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22nd Annual International Conference on Computational Science (ICCS)

作者： Bezborodov, Andrey Deeva, Irina ITMO Univ St Petersburg Russia

ISBN: (纸本)9783031087547;9783031087530

This article presents a new look at the problem of finding reservoirs-analogues, representing reservoirs as a network and solving the problem of finding reservoirs-analogues as a problem of finding communities in the network. The proposed network approach allows us to effectively search for a cluster of reservoirs-analogues and restore missing parameters in the target reservoir based on the found clusters of reservoirs-analogues. Also, the network approach was compared with the baseline approach and showed greater efficiency. Three approaches were also compared to restore gaps in the target reservoir using clusters of reservoirs-analogues.

关键词： Networks community detection algorithms Distance metrics Oil and gas reservoirs Reservoirs-analogues

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Automatic Creation and Analysis of a Linked Data Cloud Diagram 17th

Automatic Creation and Analysis of a Linked Data Cloud Diagr...

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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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Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers 28th

Quantifying Opinion Rejection: A Method to Detect Social Med...

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28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Perera, Kushani Karunasekera, Shanika Univ Melbourne Melbourne Vic 3010 Australia

ISBN: (纸本)9789819722655;9789819722662

Social media echo chambers are known to be common sources of misinformation and harmful ideologies that have detrimental impacts on society. Therefore, techniques to detect echo chambers are of great significance. Reinforcement of supporting opinions and rejection of dissenting opinions are two significant echo chamber properties that help detecting them in social networks. However, existing echo chamber detection methods do not capture the opinion rejection behaviour, which leads to poor echo chamber detection accuracy. Measures used by them do not facilitate quantifying both properties simultaneously while preserving the connectivity between echo chamber members. To address this problem, we propose a new measure, Signed Echo (SEcho) that quantifies opinion reinforcement and rejection properties of echo chambers and an echo chamber detection algorithm, Signed Echo detection Algorithm (SEDA) based on this measure, which preserves the connectivity among echo chamber members. The experimental results for real-world data show that SEDA outperforms the state-of-the-art echo chamber detection methods in detecting the communities with echo chamber properties, such as reinforcement of supporting opinions, rejection of dissenting opinions, connectivity between community members, spread of mis/disinformation and emotional contagion.

关键词： Echo chambers community detection algorithms Social networks Quantitative metrics

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Determining the best algorithm to detect community structures in networks: application to power systems

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Environment Systems and Decisions 2022年第2期42卷 251-264页

作者： Rocco, Claudio M. Barker, Kash Moronta, Jose Facultad de Ingeniería Universidad Central de Venezuela Caracas Venezuela School of Industrial and Systems Engineering University of Oklahoma 202 W. Boyd St. Rm. 124 Norman 73019 OK United States Departamento de Tecnología Industrial Universidad Simón Bolívar Caracas Venezuela

A common feature of many networks is the presence of communities, or groups of relatively densely connected nodes with sparse connections between groups. An understanding of community structures could enable the network design for improved system performance. For electric power systems, most work in the detection of community structures (i) selects a specific algorithm to perform the detection of communities (or compares a proposed algorithm against algorithms), and (ii) focuses on topological information about the networks. The objective of this article is to provide a framework to improve the selection of appropriate community detection algorithms for a family of networks with similar structures. We propose an approach to determine the most effective community detection algorithm for a set of networks and compare which algorithms provide the most similar partitions across these networks. To illustrate the comparison of various community detection algorithms, 16 electric power systems are analyzed. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： community detection algorithms Multi-criteria decision analysis Power systems Rank aggregation Sensitivity analysis TOPSIS

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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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Discovery of Evolving Relationships of Software Vulnerabilities 6

Discovery of Evolving Relationships of Software Vulnerabilit...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Sparks, Hailey Ghosh, Krishnendu College of Charleston Department of Computer Science SC United States

ISBN: (纸本)9798350386745

Discovering the risks posed by software vulnerabilities is a challenge. Software vulnerabilities are often not listed and studies have shown 50.3% of the reports do not include the list of vulnerable libraries. Thus, it becomes critical to maximize the understanding of the vulnerability reports and the trends in vulnerabilities. In this work, a novel tool is created that is able to connect the vulnerabilities with time and identify subsets of vulnerabilities that are *** the first step of the model, a text network is created from the vulnerability reports of each month in a year. Then, a temporal network is constructed from text networks. Temporal network theoretic properties are evaluated on the model to understand the vulnerability trends and the evolution of relationships amongst the vulnerabilities. The analysis leverages on the community detection algorithms on text networks. The dynamics of evolving relationships of the software vulnerabilities are extracted using biclustering algorithms, and the statistical significance of the biclusters is evaluated. Experimental results based on vulnerability reports are analyzed and presented. © 2024 IEEE.

关键词： Biclustering algorithms community detection algorithms Temporal Networks Text Graphs Vulnerability Reports

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