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A geo-location and trust-based framework with community detection algorithms to filter attackers in 5G social networks

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WIRELESS NETWORKS 2024年第6期30卷 4841-4849页

作者： Kaur, Davinder Uslu, Suleyman Durresi, Mimoza Durresi, Arjan Indiana Univ Purdue Univ Dept Comp & Informat Sci Indianapolis IN 46202 USA European Univ Tirana Tirana Albania

We propose a geographical location and trust-based framework combined with community detection algorithms to filter communities of malicious users in 5G social networks. This framework utilizes geo-location information, community trust within the network and AI community detection algorithms to identify users that can cause harm. It has a benefit over some other fake user detection mechanisms because it takes into account the characteristics that a malicious user cannot easily fake like the geographical location and community trust built throughout time. We illustrate the proposed framework on synthetic social network data. Results show this framework can distinguish potential malicious users from trustworthy users based on their location, trust, and structural attributes.

关键词： Trust Malicious users community detection algorithms 5G systems

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Analyzing evolutionary optimization and community detection algorithms using regression line dominance

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INFORMATION SCIENCES 2017年 396卷 185-201页

作者： Biswas, Anupam Biswas, Bhaskar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

In this paper, a visual analysis methodology is proposed to perform comparative analysis of guided random algorithms such as evolutionary optimization algorithms and community detection algorithms. Proposed methodology is designed based on quantile-quantile plot and regression analysis to compare performance of one algorithm over other algorithms. The methodology is extrapolated as one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, the many-to-many comparison i.e. ranking of algorithins is done only with solution quality. On the contrary, with proposed methodology ranking of algorithms is done in terms of both solution quality and convergence rate. Proposed methodology is studied with four evolutionary optimization algorithms on 25 benchmark functions. A non parametric statistical analysis called Wilcoxon signed-rank test is also performed to verify the indication of proposed methodology. Moreover, methodology is also applied to analyze four state-of-the-art community detection algorithms on 10 real-world networks. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Evolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis community detection algorithms

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Identifying beliefs about the gender gap in engineering professions among university students using community detection algorithms and statistical analysis

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2024年第4期32卷

作者： Merayo, Noemi Ayuso, Alba Univ Valladolid Sch Telecommun Engn Dept Signal & Commun Theory & Telematics Engn Valladolid 47011 Spain Univ Valladolid Fac Med Dept Pedag Valladolid Spain

Digital societies require professionals in the Technology and Engineering sectors, but their lack, particularly of women, requires a thorough understanding of this gender gap. This research analyzes the beliefs and opinions of university engineering students about the gender gap in their professional fields by means of a community detection algorithm to identify groups of students with similar belief patterns. This study leverages a community detection algorithm to analyze the beliefs of 590 engineering students regarding the gender gap in their field, together with a correlational and explanatory design using a quantitative paradigm. A validated questionnaire focusing on the professional dimension was used. The algorithm identified three student communities, two gender-sensitive and one gender-insensitive. The study uncovered a concerning lack of awareness regarding the gender gap among engineering students. Many participants did not recognize the importance of increasing the representation of professional women, maintained the belief that the gender gap affects only women, and assumed that men and women are equally paid. However, women show a higher level of awareness, while men perceive the gender gap as a passing trend, which is worrying. Students recognize the importance of integrating a gender perspective into university and engineering curricula. It is worrying that many students doubt the existence of the gender gap and that both genders lack knowledge about gender gap issues. Finally, community detection algorithms could efficiently and automatically analyze gender gap issues or other unrelated topics.

关键词： community detection algorithms engineering education engineering workforce gender equality professional dimension STEM

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Topology of Complex Networks and Performance Limitations of community detection algorithms

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IEEE ACCESS 2017年 5卷 10901-10914页

作者： Pasta, Muhammad Qasim Zaidi, Faraz Karachi Inst Econ & Technol Karachi 75190 Pakistan Usman Inst Technol Karachi 75300 Pakistan Reg Peel Toronto ON L6T 4B9 Canada

One of the most widely studied problems in the analysis of complex networks is the detection of community structures. Many algorithms have been proposed to find communities but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from various domains. As the topology of networks changes with respect to domains, community detection algorithms fail to accommodate these changes to detect communities. In this paper, we attempt to highlight this problem by studying networks with different topologies and evaluate the performance of community detection algorithms in the light of these topological changes. To generate networks with different topologies, we used the well-known Lancichinetti-Fortunato-Radicchi (LFR) model, and we also propose a new model named Naive Scale-Free Clustering to avoid any bias that can be introduced by the underlying network generation model. Results reveal several limitations of the current popular network clustering algorithms failing to correctly find communities. This suggests the need to revisit the design of current clustering algorithms in order to improve their performances.

关键词： Social networks and graphs network topology benchmark networks community detection algorithms normalized mutual information

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Towards Designing Secure In-Vehicle Network Architectures Using community detection algorithms

Towards Designing Secure In-Vehicle Network Architectures Us...

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IEEE Vehicular Networking Conference (VNC)

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

ISBN: (纸本)9781479976607

Efforts in securing the in-vehicle network have resulted in a number of proposed security mechanisms in recent years. However, so far little attention has been given to the actual architecture of the in-vehicle network. An approach within in-vehicle network design is to divide the network into domains, where each domain consists of a set of Electronic Control Units (ECUs) that handles some united functionality, e.g., body control, powertrain, and telematics. Still, this approach is based on "best engineering practice" and there is room for improvements. In this paper, we study real traffic from a modern car and we try to divide the in-vehicle network into domains using automated partitioning algorithms. To find the optimum division, we select four community detection algorithms, known from social network analysis, and we evaluate their ability to find these domains. We conclude that community detection algorithms can be used to identify in-vehicle domains based on the message types (signals) used in the in-vehicle network and we demonstrate this by applying the algorithms to real data. The approach is not limited to only message types, but domains can also be identified based on other criteria, such as frequency of messages, payload sizes, or Automotive Safety Integrity Levels (ASILs). We also conclude that the identification of good domains can facilitate the implementation of security measures. Therefore, we believe that the approach has great potential to help engineers in deriving secure in-vehicle network architectures during the design of a vehicle.

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

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The European community of Law and the Communities of Case-law: Understanding legal concepts and processes through the lens of community detection algorithms

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European Law Open 2024年第2期3卷 431-455页

作者： Šadl, Urška López Zurita, Lucía Piccolo, Sebastiano European University Institute Fiesole Italy MOBILE University of Copenhagen Copenhagen Denmark University of Calabria Rende Italy

community detection is a set of algorithms developed in network science to find meaningful sub-groups within larger groups. This article (1) outlines and evaluates the method and (2) shows how it can enrich ongoing debates about European integration. To this end, it uses the example of the approximation of laws, an enduring topic in European legal studies. © The Author(s), 2024.

关键词： approximation of laws case-law community detection algorithms European Court of Justice European integration network approach

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A comprehensive Evaluation of community detection algorithms

A comprehensive Evaluation of Community Detection algorithms

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2024 Workshop on Complexity in Engineering

作者： Aidara, Ndeye Khady Diop, Issa Moussa Diallo, Cherif Cherifi, Hocine Gaston Berger Univ LACCA LAB St Louis Senegal Univ Cote Azur I3S DS4H Nice France Univ Bourgogne ICB UMR CNRS 6303 F-21078 Dijon France

ISBN: (纸本)9798350382808;9798350382792

In many domains, including social network analysis, biology, and beyond, community structure discovery in networks is a basic topic. To find highly connected node groups-often referred to as communities-in these networks, researchers develop a wide range of techniques. While the field evolves rapidly, many comparative studies are carried out to assess the strengths and weaknesses of different algorithms on various types of networks and scenarios. This study attempts to provide a thorough comparative analysis involving both established techniques like Louvain, Label Propagation, Infomap, Girvan-Newman, and Walktrap, as well as newly proposed methods like PercoMVC, Walkscan, and Paris. This is due to the constantly changing landscape of community detection algorithms and the critical importance of understanding their strengths and weaknesses. We carry out a thorough empirical assessment of these eight community recognition methods on a variety of real-world networks, such as biological, social, and collaborative networks, among others with different topologies. A wide range of internal and external quality criteria, including conductance, density-based measurements, modularity, cut ratio, and the Friedman rank test, are incorporated into our analysis. Louvain consistently performs well on dense networks and is the best at optimizing modularity. Large, sparse networks work best with label propagation, effectively reducing the number of intercommunity links. PercoMVC shows high overall resilience and adaptability across a variety of network configurations. Infomap is good at capturing complex community structures and works well on various network types. Walktrap provides a solid balance between accuracy and efficiency and demonstrated high performance on congested networks. Our study provides insights to guide practitioners in selecting the most suitable community detection technique based on the network characteristics at hand, highlighting the complementary strengths

关键词： community detection algorithms community structure Network analysis

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Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

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BEHAVIOR RESEARCH METHODS 2024年第7期56卷 7219-7240页

作者： Santiago, Pedro Henrique Ribeiro Soares, Gustavo Hermes Quintero, Adrian Jamieson, Lisa Univ Adelaide Adelaide Dent Sch Level 450 Rundle MallRundle Mall Plaza Adelaide Australia Colombian Inst Educ Evaluat ICFES Bogota Colombia

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities);and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous);(2) number of factors (two and four);(3) number of observed variables per factor (four and eight);(4) factor correlations (0.0, 0.5, and 0.7);(5) size of primary factor loadings (0.40, 0.55, and 0.70);(6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%);and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

关键词： Network psychometrics Clique percolation community detection algorithms Overlapping symptoms Fuzzy Modularity

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Comparative analysis of sequential community detection algorithms based on internal and external quality measure

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JOURNAL OF STATISTICS AND MANAGEMENT SYSTEMS 2020年第7期23卷 1129-1146页

作者： Singh, Dipika Garg, Rakhi Banaras Hindu Univ Inst Sci Dept Comp Sci Varanasi 221005 Uttar Pradesh India Banaras Hindu Univ Dept Comp Sci Mahila Mahavidyalaya Varanasi 221005 Uttar Pradesh India

community detection is a dominant research field in social media mining. Numerous algorithms have been developed to detect community structure in network. Each of them focuses on different characteristic of data and generates different communities for same dataset. So cluster validation or comparative analysis of these algorithms is required to compare the efficiency of these algorithms. There are two known ways for cluster validation: Internal Quality Measure (IQM) and External Quality Measure (EQM). IQM evaluates the cluster without reference to external information while EQM requires already known clusters i.e. ground truth for comparison. Most of the researchers have compared community detection algorithms on the basis of either IQM or EQM but not both. In this paper, we have tried to compare nine existing sequential community detection algorithms on the basis of both IQM and EQM and we observed that overall performance of Louvain, Fastgreedy and Walktrap algorithm is more efficient than other algorithms. From this paper, researchers working in this area can get information about major sequential community detection algorithms and their efficiency on small real world datasets. They can incorporate the findings to develop more efficient algorithms.

关键词： community detection algorithms community detection data sets Cluster validation Internal quality measure External quality measure

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Improved relation extraction through key phrase identification using community detection on dependency trees

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COMPUTER SPEECH AND LANGUAGE 2025年 89卷

作者： Liu, Shuang Chen, Xunqin Meng, Jiana Lukac, Niko Dalian Minzu Univ Sch Comp Sci & Engn Dalian 116600 Peoples R China Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia

A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods.

关键词： community detection algorithms Dependency trees Relation extraction

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