Relevant bibliographies by topics / Graph community detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Graph community detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Graph community detection"

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Bapuji, Rao, and Mishra Sarojananda. "Detection of Sub-Community Graph in N-Community Graphs using Graph Mining." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2014–23. https://doi.org/10.35940/ijeat.B4530.029320.

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Detection of sub-graphs in community graphs is an important task and useful for characterizing community graphs. This characterization leads to classification as well as clusterings of community graphs. It also leads to finding differences among a set of community graphs as well as buildings of indices of community graphs. Finally, this characterization leads discovery of knowledge from sub-graphs. This proposed approach of detection of a sub-community graph from a group of community graphs using simple graph theory techniques. So, that knowledge could be discovered from the sub-community grap
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Huang, Zhaoci, Wenzhe Xu, and Xinjian Zhuo. "Community-CL: An Enhanced Community Detection Algorithm Based on Contrastive Learning." Entropy 25, no. 6 (2023): 864. http://dx.doi.org/10.3390/e25060864.

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Graph contrastive learning (GCL) has gained considerable attention as a self-supervised learning technique that has been successfully employed in various applications, such as node classification, node clustering, and link prediction. Despite its achievements, GCL has limited exploration of the community structure of graphs. This paper presents a novel online framework called Community Contrastive Learning (Community-CL) for simultaneously learning node representations and detecting communities in a network. The proposed method employs contrastive learning to minimize the difference in the lat
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Wang, Guishen, Yuanwei Wang, Kaitai Wang, et al. "An overlapping community detection algorithm based on node distance of line graph." Modern Physics Letters B 33, no. 26 (2019): 1950322. http://dx.doi.org/10.1142/s0217984919503226.

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Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula
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Rao, Bapuji, and Sarojananda Mishra. "A New Approach to Community Graph Partition Using Graph Mining Techniques." International Journal of Rough Sets and Data Analysis 4, no. 1 (2017): 75–94. http://dx.doi.org/10.4018/ijrsda.2017010105.

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Knowledge extraction is very much possible from the community graph using graph mining techniques. The authors have studied the related definitions of graph partition in terms of both mathematical as well as computational aspects. To derive knowledge from a particular sub-community graph of a large community graph, the authors start partitioning the large community graph into smaller sub-community graphs. Thus, the knowledge extraction from the sub-community graph becomes easier and faster. The proposed approach of partition is done by detection of edges among the community members of dissimil
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Lim, Bo-Young, Jeong-Ha Park, Kisung Lee, and Hyuk-Yoon Kwon. "Multi-Level Graph Representation Learning Through Predictive Community-based Partitioning." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–27. https://doi.org/10.1145/3711115.

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Graph representation learning (GRL) aims to map a graph into a low-dimensional vector space while preserving graph topology and node properties. This study proposes a novel GRL model, Multi-Level GRL (simply, ML-GRL), that recursively partitions input graphs by selecting the most appropriate community detection algorithm at each graph or partitioned subgraph. To preserve the relationship between subgraphs, ML-GRL incorporates global graphs that effectively maintain the overall topology. ML-GRL employs a prediction model, which is pre-trained using graph-based features and covers a wide range o
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Muñoz, Héctor, Eloy Vicente, Ignacio González, Alfonso Mateos, and Antonio Jiménez-Martín. "ConvGraph: Community Detection of Homogeneous Relationships in Weighted Graphs." Mathematics 9, no. 4 (2021): 367. http://dx.doi.org/10.3390/math9040367.

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This paper proposes a new method, ConvGraph, to detect communities in highly cohesive and isolated weighted graphs, where the sum of the weights is significantly higher inside than outside the communities. The method starts by transforming the original graph into a line graph to apply a convolution, a common technique in the computer vision field. Although this technique was originally conceived to detect the optimum edge in images, it is used here to detect the optimum edges in communities identified by their weights rather than by their topology. The method includes a final refinement step a
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Agrawal, Smita, and Atul Patel. "Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review." Recent Advances in Computer Science and Communications 13, no. 4 (2020): 542–49. http://dx.doi.org/10.2174/2213275912666190710183635.

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Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the dense
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Newman, M. E. J. "Community detection and graph partitioning." EPL (Europhysics Letters) 103, no. 2 (2013): 28003. http://dx.doi.org/10.1209/0295-5075/103/28003.

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Gargi, Ullas, Wenjun Lu, Vahab Mirrokni, and Sangho Yoon. "Large-Scale Community Detection on YouTube for Topic Discovery and Exploration." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 486–89. http://dx.doi.org/10.1609/icwsm.v5i1.14191.

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Detecting coherent, well-connected communities in large graphs provides insight into the graph structure and can serve as the basis for content discovery. Clustering is a popular technique for community detection but global algorithms that examine the entire graph do not scale. Local algorithms are highly parallelizable but perform sub-optimally, especially in applications where we need to optimize multiple metrics. We present a multi-stage algorithm based on local-clustering that is highly scalable, combining a pre-processing stage, a lo- cal clustering stage, and a post-processing stage. We
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Sun, Heli, Yang Li, Bing Lv, et al. "Graph Community Infomax." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (2022): 1–21. http://dx.doi.org/10.1145/3480244.

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Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same tim
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Dissertations / Theses on the topic "Graph community detection"

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Djuphammar, Felix. "Efficient graph embeddings with community detection." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185134.

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Networks are useful when modeling interactions in real-world systems based on relational data. Since networks often contain thousands or millions of nodes and links, analyzing and exploring them requires powerful visualizations. Presenting the network nodes in a map-like fashion provides a large scale overview of the data while also providing specific details. A suite of algorithms can compute an appropriate layout of all nodes for the visualization. However, these algorithms are computationally expensive when applied to large networks because they must repeatedly derive relations between ever
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Alsahafy, Maram Saad M. "Efficient Algorithms for Speeding Up Graph Data Analytics." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26166.

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Graph model has been playing an important role in analyzing the data from real applications such as social networks, communication networks, and information networks. It models entities of the applications as vertices/nodes in the graph, and models relationships among the entities as edges between vertices in the graph. In recent years there has been an increasing number of studies of complex graph analysis coinciding with the rapid development of information technologies, such as online social networks and (mobile/email) communication networks. Due to the growing sizes of these graph data,
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Nastos, James. "Utilizing graph classes for community detection in social and complex networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/53014.

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Social network analysis is a cross-disciplinary study of interest to mathematicians, physicists, computer scientists and sociologists. It deals with looking at large networks of interactions and extracting useful or meaningful information from them. One attribute of interest is that of identifying social communities within a network: how such a substructure should be defined is a widely-studied problem in itself. With each new definition, there is a need to study in what applications or context such a definition is appropriate, and develop algorithms and complexity results for the computation
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Geffrier, Valentin. "Community Detection applied to Cross-Device Identity Graphs." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216963.

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The personalization of online advertising has now become a necessity for marketing agencies. The tracking technologies such as third-party cookies gives advertisers the ability to recognize internet users across different websites, to understand their behavior and to assess their needs and their tastes. The amount of created data and interactions leads to the creation of a large cross-device identity graph that links different identifiers such as emails to different devices used on different networks. Over time, strongly connected components appear in this graph, too large to represent only th
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Weigert, Stefan. "Community-Based Intrusion Detection." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-217677.

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Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the adversaries manage to steal important data or disrupt vital services. Victims are preferably companies from v
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Psorakis, Ioannis. "Probabilistic inference in ecological networks : graph discovery, community detection and modelling dynamic sociality." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:84741d8b-31ea-4eee-ae44-a0b7b5491700.

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This thesis proposes a collection of analytical and computational methods for inferring an underlying social structure of a given population, observed only via timestamped occurrences of its members across a range of locations. It shows that such data streams have a modular and temporally-focused structure, neither fully ordered nor completely random, with individuals appearing in "gathering events". By exploiting such structure, the thesis proposes an appropriate mapping of those spatio-temporal data streams to a social network, based on the co-occurrences of agents across gathering events, w
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Wengle, Emil. "Modelling Hierarchical Structures in Networks Using Graph Theory : With Application to Knowledge Networks in Graph Curricula." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415044.

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Community detection is a topic in network theory that involves assigning labels to nodes based on some distance measure or centrality index. Detecting communities within a network can be useful to perform information condensation. In this thesis we explore how to use the approach for pedagogical purposes, and more precisely to condense and visualise the networks of facts, concepts and procedures (also called Knowledge Components (KCs)) that are offered in higher education programmes. In details, we consider one of the most common quantities used to evaluate the goodness of a community classifi
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Sattar, Naw Safrin. "Scalable Community Detection using Distributed Louvain Algorithm." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2640.

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Community detection (or clustering) in large-scale graph is an important problem in graph mining. Communities reveal interesting characteristics of a network. Louvain is an efficient sequential algorithm but fails to scale emerging large-scale data. Developing distributed-memory parallel algorithms is challenging because of inter-process communication and load-balancing issues. In this work, we design a shared memory-based algorithm using OpenMP, which shows a 4-fold speedup but is limited to available physical cores. Our second algorithm is an MPI-based parallel algorithm that scales to a mod
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Kim, Sungmin. "Community Detection in Directed Networks and its Application to Analysis of Social Networks." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397571499.

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Abdelsadek, Youcef. "Triangle packing for community detection : algorithms, visualizations and application to Twitter's network." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0310.

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De nos jours, nous générons une quantité immensément grande de données juste en accomplissant nos simples tâches quotidiennes. L'analyse de ces données soulève des challenges ardus. Dans cette thèse, nous nous intéressons à deux aspects des données relationnelles. En premier lieu, nous considérons les données relationnelles dans lesquelles les relations sont pondérées. Un exemple concret serait le nombre commun de suiveurs entre deux utilisateurs de Twitter. Dans un deuxième temps, nous abordons le cas dynamique de ces données qui est inhérent à leur nature. Par exemple, le nombre de suiveurs
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Books on the topic "Graph community detection"

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Halappanavar, Mahantesh, Karthi Duraisamy, Hao Lu, Partha Pratim Pande, and Ananth Kalyanaraman. Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms. Now Publishers, 2016.

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Newman, Mark. Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.001.0001.

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The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, an
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Bianconi, Ginestra. The Structure of Single Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0002.

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Chapters 2–3 constitute Part II of the book, ‘Single Networks’, and provide a reference point for the rest of the book devoted exclusively to Multilayer Networks, making the book self-contained. This chapter provides the relevant background on the network structure of complex networks formed by just one layer (single networks). Here the basic definitions of network structure are given, the major network universalities are presented and methods to extract relevant information from network structure including centrality measures and community detection methods are discussed. Finally, modelling f
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Book chapters on the topic "Graph community detection"

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Chakrabarti, D., and C. Faloutsos. "Community Detection." In Graph Mining. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01903-6_16.

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Kissgen, Maximilian, Joachim Allgaier, and Ralf Klamma. "Child Influencers on YouTube: From Collection to Overlapping Community Detection." In Graph Databases. CRC Press, 2023. http://dx.doi.org/10.1201/9781003183532-5.

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Laeuchli, Jesse. "Fast Community Detection with Graph Sparsification." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_23.

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Sun, Xu, Weiyu Zhang, Zhengkai Wang, and Wenpeng Lu. "Variational Graph Embedding for Community Detection." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1645-0_57.

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Greffard, Nicolas, Fabien Picarougne, and Pascale Kuntz. "Visual Community Detection: An Evaluation of 2D, 3D Perspective and 3D Stereoscopic Displays." In Graph Drawing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25878-7_21.

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Meena, Shyam Sundar, and Vrinda Tokekar. "Sentiment-Based Community Detection Using Graph Transformation." In Advances in Data-driven Computing and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0981-0_6.

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Abonyi, János, László Nagy, and Tamás Ruppert. "Network Community Detection Algorithm for Graph Networks." In Springer Series in Advanced Manufacturing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47444-6_8.

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Zoghlemi, Chiheb Edine, and Abdelkerim Rezgui. "Contextual Multi-View Graph Community Detection Using Graph Neural Networks." In Artificial Intelligence Tools and Applications in Embedded and Mobile Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56576-2_5.

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Paul, Sudipta, Julián Salas, and Vicenç Torra. "Edge Local Differential Privacy for Dynamic Graphs." In Security and Privacy in Social Networks and Big Data. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5177-2_13.

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AbstractHuge amounts of data are generated and shared in social networks and other network topologies. This raises privacy concerns when such data is not protected from leaking sensitive or personal information. Network topologies are commonly modeled through static graphs. Nevertheless, dynamic graphs better capture the temporal evolution and properties of such networks. Several differentially private mechanisms have been proposed for static graph data mining, but at the moment there are no such algorithms for dynamic data protection and mining. So, we propose two locally $$\epsilon $$ ϵ -dif
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Ma, Xuebin, Jingyu Yang, and Shengyi Guan. "Differentially Private Social Graph Publishing for Community Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63095-9_11.

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Conference papers on the topic "Graph community detection"

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Li, Mingjiao, Xing Chu, Miao Luo, Haiyang Zhao, and Hanxing Jiang. "Masked Dual Graph Autoencoder for Attributed Graph Community Detection." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650660.

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Yang, Han, Lingxiang Wang, Qingren Wang, et al. "Local Community Detection from Noisy Graph Streams." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762534.

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Çakir, Şevket Umut, Mehmet Ali Osman Atik, and Ümit Deniz Uluşar. "Community Detection on Software Library Dependency Graphs using Graph Neural Networks." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773551.

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Yang, Ran, Xiaoyan Zheng, Yiwen Shen, and Zhiya Zhou. "Community Detection Algorithm based on Graph Capsule Networks." In 2024 6th Asia Symposium on Image Processing (ASIP). IEEE, 2024. http://dx.doi.org/10.1109/asip63198.2024.00009.

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Li, HongBin, Fanyu Han, and Wei Wang. "Cluster-Perceptive Graph Contrastive Learning for Community Detection." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889718.

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Shen, Yiwen, Xiaoyan Zheng, and Ran Yang. "Community Detection Algorithm Based On High-Order Variational Graph Autoencoder." In 2024 6th Asia Symposium on Image Processing (ASIP). IEEE, 2024. http://dx.doi.org/10.1109/asip63198.2024.00029.

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Wang, Ping, Fengrui Xiao, Yudan Wang, Shuangwu Chen, and Feng Yang. "Graph Attention Autoencoder Aided Unsupervised Community Detection for Relieving Alert Fatigue." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10941774.

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Devesh Kumar, V. V., S. Sreesankar, Krishnpriya Dinesan, Pranav B. Nair, and L. R. Deepthi. "Analyzing GNN Models for Community Detection Using Graph Embeddings: A Comparative Study." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725530.

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Krishnan, Anusuya, and Isaias Mehari Ghebrehiwet. "GCD-TM: Graph-Driven Community Detection for Topic Modelling in Psychiatry Texts." In Proceedings of the 1st Workshop on NLP for Science (NLP4Science). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.nlp4science-1.6.

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Chen, Sirry, Shuo Feng, Liang Songsong, Chen-Chen Zong, Jing Li, and Piji Li. "CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.617.

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