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Modularity Based community detection in Hypergraphs 1

18th International Workshop on algorithms and Models for the Web Graph (WAW)

作者： Kaminski, Bogumil Misiorek, Pawel Pralat, Pawel Theberge, Francois SGH Warsaw Sch Econ Decis Anal & Support Unit Warsaw Poland Poznan Univ Tech Inst Comp Sci Poznan Poland Toronto Metropolitan Univ Dept Math Toronto ON Canada Tutte Inst Math & Comp Ottawa ON Canada

ISBN: (数字)9783031322969

ISBN: (纸本)9783031322952;9783031322969

In this paper, we make a significant step toward designing a scalable community detection algorithm using hypergraph modularity function. The main obstacle with adjusting the initial stage of the classical Louvain algorithm is dealt via carefully adjusted linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. It remains to properly tune the algorithm and design a mechanism to adjust the weights in the modularity function (in an unsupervised way), depending on how often nodes in one community share hyperedges with nodes from other communities. It will be done in the journal version of this paper.

关键词： community detection algorithm Hypergraphs Modularity Function

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Enhancing Participation of Widespread Distributed Energy Storage Systems in Frequency Regulation Through Partitioning-Based Control

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PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 2025年第1期10卷 76-89页

作者： Lin, Yujun Luo, Haowen Chen, Yin Yang, Qiufan Zhou, Jianyu Chen, Xia Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China State Grid Jingmen Power Supply Co Jingmen 448000 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

In recent years, a significant number of distributed small-capacity energy storage (ES) systems have been integrated into power grids to support grid frequency regulation. However, the challenges associated with high-dimensional control and synergistic operation alongside conventional generators remain unsolved. In this paper, a partitioning-based control approach is developed for the participation of widespread distributed ES systems on frequency control in power systems. The approach comprises a network partitioning method and a two-layer frequency control scheme. The partitioning method utilizes a community detection algorithm in which the weights between the buses are calculated based on the electrical distances. After partitioning the buses into different groups, an optimization-based frequency control system with two layers is established to aggregate and dis-aggregate the inertia and droop coefficients so that frequency regulation and economical operation can be achieved. The effectiveness of the proposed method is demonstrated through numerical simulations on an IEEE 39-bus system. The results confirm the successful elimination of frequency deviations and low operating cost of the proposed approach.

关键词： Costs Power systems Topology Reliability detection algorithms Sustainable development Protection Frequency control Energy storage Resilience community detection algorithm distributed control energy storage frequency response

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Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling

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MULTIVARIATE BEHAVIORAL RESEARCH 2025年第2期60卷 184-210页

作者： Shi, Dingjing Christensen, Alexander P. Day, Eric Anthony Golino, Hudson F. Garrido, Luis Eduardo Univ Oklahoma Dept Psychol Norman OK 73109 USA Vanderbilt Univ Dept Psychol & Human Dev Nashville TN USA Univ Virginia Dept Psychol Charlottesville VA USA Pontificia Univ Catolica Madre & Maestra Santiago Los Caballeros Dominican Rep

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., ***) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (***) yielded higher accuracy and smaller errors than PA;lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then *** and ***. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the *** technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the *** method as a valuable alternate technique. The findings also indicated encouraging results for extending the regulariz

关键词： Network psychometrics dimensionality assessment estimation procedures community detection algorithm Bayesian estimation

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Model-data matching method for natural disaster emergency service scenarios: implementation based on a knowledge graph and community discovery algorithm

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NATURAL HAZARDS 2024年第5期120卷 4233-4255页

作者： Liu, Honghao Hu, Zhuowei Yang, Ziqing Wang, Mi Capital Normal Univ Coll Resources Environm & Tourism Beijing Peoples R China

Massive disaster-related data and model methods are available in different stages of natural disaster emergency management;however, they often lack the representation of the significance of crucial information. In addition, traditional methods are difficult to automate and fine-match, resulting in greater difficulty in intelligent applications. To address this problem, interconnected knowledge arranged in a knowledge graph and a community discovery algorithm were used to match disaster data with the appropriate method under a unified description framework. Based on the knowledge graph constructed by ontology mapping, this study aimed to represent and store the complex entities and relationships among disaster-related information. The node importance analysis and community discovery algorithm were used to analyze the community structure of important nodes, and a typical rainstorm flood disaster risk assessment emergency service scenario was taken as an example for experimental verification. The results showed that the constructed knowledge graph of natural disaster emergency services can support the formal expression of semantic correlation between disaster scenarios, model methods, and disaster data. The community discovery method performed well in overlapping community module degrees. The method proposed in this study can be used to formalize the representation and storage of massive heterogeneous data and conduct fine matching after quantitative analysis of the importance of model methods and data required for risk assessment of emergency service scenarios. Furthermore, the proposed method can provide theoretical support for improving the scientificity, accuracy, and interpretability of risk assessment decision management and promoting knowledge-driven intelligent emergency information service capabilities.

关键词： Disaster data matching Knowledge graph community detection algorithm Emergency risk assessment Ontology mapping

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community detection in multi-relational directional networks

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 67卷

作者： Roozbahani, Zahra Rezaeenour, Jalal Katanforoush, Ali Univ Qom Fac Technol & Engn Dept Comp Engn & IT Qom Iran Univ Qom Fac Technol & Engn Dept Ind Engn Qom Iran Shahid Beheshti Univ Fac Math Sci Dept Data & Comp Sci Tehran Iran

community detection algorithms are essential tools that allow us to discover organizational principles in net-works. Today, in most cases, social networks are modeled as a multi-relational network. So far, despite the introduction of community detection algorithms specific to multi-relational networks, very few of these algo-rithms have been able to find overlapping communities in a multi-relational directional network. In this paper, we present OCMRN (Overlapping Communities in Multi-Relational Networks), an overlapping community detection method in multi-relational directional networks. A semi-supervised method is used in the training phase of this algorithm which allows determining the importance of each layer to shape communities. The proposed method is evaluated on nine real and eight synthetic datasets and is compared with different algo-rithms. Well-known evaluation criteria are used to compare overlapping and non-overlapping communities resulting from various algorithms. The high accuracy of the results obtained from the proposed model suggests this algorithm can be used with high confidence for community detection of multi-relational directional net-works to find overlapping communities.

关键词： Multi-Relational Network Overlapping Communities community detection algorithm Probabilistic Model

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Policy Gradient-Based Core Placement Optimization for Multichip Many-Core Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4529-4543页

作者： Myung, Wooshik Lee, Donghyun Song, Chenhang Wang, Guanrui Ma, Cheng Tsinghua Univ Dept Precis Instrument Ctr Brain Inspired Comp Res Beijing 100084 Peoples R China

As many deep neural network models become deeper and more complex, processing devices with stronger computing performance and communication capability are required. Following this trend, the dependence on multichip many-core systems that have high parallelism and reasonable transmission costs is on the rise. In this work, in order to improve routing performance of the system, such as routing runtime and power consumption, we propose a reinforcement learning (RL)based core placement optimization approach, considering application constraints, such as deadlock caused by multicast paths. We leverage the capability of deep RL from indirect supervision as a direct nonlinear optimizer, and the parameters of the policy network are updated by proximal policy optimization. We treat the routing topology as a network graph, so we utilize a graph convolutional network to embed the features into the policy network. One step size environment is designed, so all cores are placed simultaneously. To handle large dimensional action space, we use continuous values matching with the number of cores as the output of the policy network and discretize them again for obtaining the new placement. For multichip system mapping, we developed a community detection algorithm. We use several datasets of multilayer perceptron and convolutional neural networks to evaluate our agent. We compare the optimal results obtained by our agent with other baselines under different multicast conditions. Our approach achieves a significant reduction of routing runtime, communication cost, and average traffic load, along with deadlock-free performance for inner chip data transmission. The traffic of interchip routing is also significantly reduced after integrating the community detection algorithm to our agent.

关键词： Optimization Routing Training System recovery Power demand Neural networks Topology community detection algorithm deadlock-free placement physical mapping reinforcement learning (RL)

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NUVER: Network Based Vulnerability Visualizer 30

NUVER: Network Based Vulnerability Visualizer

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IEEE 30th Annual Software Technology Conference (STC)

作者： Sparks, Hailey Ghosh, Krishnendu Coll Charleston Dept Comp Sci Charleston SC 29424 USA

ISBN: (纸本)9798350319415

The deluge of attacks on IT infrastructure has necessitated the understanding of cybersecurity vulnerabilities for mitigation. A two-step novel model is developed to harness the time-stamped data to analyze the vulnerability trends. In the first step of the model, a text network is created from the vulnerability report. A software is constructed that analyzes vulnerabilities by leveraging natural language processing and network theory. The network theoretic study provides details of the complex relationships between vulnerabilities. The visualizations from the tool provide information for security researchers for an in-depth study of vulnerabilities.

关键词： community detection algorithm Natural Language Processing Network Theory Software Vulnerabilities

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An interaction-based method for detecting overlapping community structure in real-world networks

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022年第1期14卷 27-44页

作者： Kumar, Pawan Dohare, Ravins Indira Gandhi Natl Open Univ Sch Sci New Delhi India Jamia Millia Islamia Ctr Interdisciplinary Res Basic Sci New Delhi 110025 India

A central theme of network analysis, these days, is the detection of community structure as it offers a coarse-grained view of the network at hand. A more interesting and challenging task in network analysis involves the detection of overlapping community structure due to its wide-spread applications in synthesising and interpreting the data arising from social, biological and other diverse fields. Certain real-world networks possess a large number of nodes whose memberships are spread through multiple groups. This phenomenon called community structure with pervasive overlaps has been addressed partially by the development of a few well-known algorithms. In this paper, we presented an algorithm called Interaction Coefficient-based Local community detection (IC-LCD) that not only uncovers the community structures with pervasive overlaps but do so efficiently. The algorithm extracted communities through a local expansion strategy which underlie the notion of interaction coefficient. We evaluated the performance of IC-LCD on different parameters such as speed, accuracy and stability on a number of synthetic and real-world networks, and compared the results with well-known baseline algorithms, namely DEMON, OSLOM, SLPA and COPRA. The results give a clear indication that IC-LCD gives competitive performance with the chosen baseline algorithms in uncovering the community structures with pervasive overlaps. The time complexity of IC-LCD is O(nc(max)), where n is the number of nodes, and c(max) is the maximum size of a community detected in a network.

关键词： Node interaction coefficient Edge interaction coefficient Pervasive overlap community detection algorithm

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Scalable Spectral Clustering for Overlapping community detection in Large-Scale Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第4期32卷 754-767页

作者： Van Lierde, Hadrien Chow, Tommy W. S. Chen, Guanrong City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

While the majority of methods for community detection produce disjoint communities of nodes, most real-world networks naturally involve overlapping communities. In this paper, a scalable method for the detection of overlapping communities in large networks is proposed. The method is based on an extension of the notion of normalized cut to cope with overlapping communities. A spectral clustering algorithm is formulated to solve the related cut minimization problem. When available, the algorithm may take into account prior information about the likelihood for each node to belong to several communities. This information can either be extracted from the available metadata or from node centrality measures. We also introduce a hierarchical version of the algorithm to automatically detect the number of communities. In addition, a new benchmark model extending the stochastic blockmodel for graphs with overlapping communities is formulated. Our experiments show that the proposed spectral method outperforms the state-of-the-art algorithms in terms of computational complexity and accuracy on our benchmark graph model and on five real-world networks, including a lexical network and large-scale social networks. The scalability of the proposed algorithm is also demonstrated on large synthetic graphs with millions of nodes and edges.

关键词： Clustering algorithms Image edge detection Benchmark testing Partitioning algorithms Social network services detection algorithms Optimization community detection algorithm overlapping communities real-world network spectral clustering normalized cut

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On Utilizing Communities Detected From Social Networks in Hashtag Recommendation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2020年第4期7卷 971-982页

作者： Alsini, Areej Datta, Amitava Huynh, Du Q. Univ Western Australia Dept Comp Sci & Software Engn Perth WA 6009 Australia Umm Al Qura Univ Dept Comp Sci Mecca 715 Saudi Arabia

Personalized recommendation automatically predicts the top-y hashtags to a given tweet. Most research in the literature of hashtag recommendation focused on the content of the posts such as words and topics. Although these methods have measured the performance of hashtag recommendation on large data sets, there is a lack of analysis on how these methods perform on small communities. Motivated by the well-studied research area of community detection algorithms that aggregate strongly connected users with similar interests and behaviors, in this article, we propose a community-based hashtag recommendation framework, which studies hashtag recommendation through tweet similarity task and applies it on communities detected using the Clique percolation method, Louvain algorithm, and label propagation method. The detected communities are extracted from four social network constructions based on following, mention, hashtag, and topic. Compared to the three state-of-the-art hashtag recommendation methods, our extensive experiments show that our community-based method outperforms these methods, thus giving a higher hit rate. Our in-depth analysis demonstrates that the performance of hashtag recommendation is the best when the communities are generated using the Clique percolation method (CPM) from the network of users who share similar usage of hashtags.

关键词： Twitter Tagging detection algorithms Australia Clustering algorithms Data mining community detection algorithm content similarity hashtag recommendation network graphs predictive models social computing

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