Relevant bibliographies by topics / Maximal Clique Size

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

Academic literature on the topic 'Maximal Clique Size'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Maximal Clique Size"

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Shen, Bin, and Yixiao Li. "Analysis of co-occurrence networks with clique occurrence information." International Journal of Modern Physics C 25, no. 05 (2014): 1440015. http://dx.doi.org/10.1142/s0129183114400154.

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Most of co-occurrence networks only record co-occurrence relationships between two entities, and ignore the weights of co-occurrence cliques whose size is bigger than two. However, this ignored information may help us to gain insight into the co-occurrence phenomena of systems. In this paper, we analyze co-occurrence networks with clique occurrence information (CNCI) thoroughly. First, we describe the components of CNCIs and discuss the generation of clique occurrence information. And then, to illustrate the importance and usefulness of clique occurrence information, several metrics, i.e. sing
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Natarajan, Meghanathan. "DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER THE WATTS-STROGATZ MODEL OF EVOLUTION OF COMPLEX NETWORKS." International Journal on Foundations of Computer Science & Technology (IJFCST) 5, no. 3 (2023): 12. https://doi.org/10.5281/zenodo.8229154.

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In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the maximal clique size per node by varying the probability of link rewiring a
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SLATER, NOA, ROYI ITZCHACK, and YORAM LOUZOUN. "Mid size cliques are more common in real world networks than triangles." Network Science 2, no. 3 (2014): 387–402. http://dx.doi.org/10.1017/nws.2014.22.

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AbstractReal world networks typically have large clustering coefficients. The clustering coefficient can be interpreted to be the result of a triangle closing mechanism. We have here enumerated cliques and maximal cliques in multiple networks to show that real world networks have a high number of large cliques. While triangles are more frequent than expected, large cliques are much more over-expressed, and the largest difference between real world networks and their random counterpart occurs in many networks at clique sizes of 5–7, and not at a size of 3. This does not result from the existenc
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Tang, Qingsong. "On Clustering Detection Based on a Quadratic Program in Hypergraphs." Journal of Mathematics 2022 (January 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/4840964.

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A proper cluster is usually defined as maximally coherent groups from a set of objects using pairwise or more complicated similarities. In general hypergraphs, clustering problem refers to extraction of subhypergraphs with a higher internal density, for instance, maximal cliques in hypergraphs. The determination of clustering structure within hypergraphs is a significant problem in the area of data mining. Various works of detecting clusters on graphs and uniform hypergraphs have been published in the past decades. Recently, it has been shown that the maximum 1,2 -clique size in 1,2 -hypergrap
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Deng, Wen, Weiguo Zheng, and Hong Cheng. "Accelerating Maximal Clique Enumeration via Graph Reduction." Proceedings of the VLDB Endowment 17, no. 10 (2024): 2419–3431. http://dx.doi.org/10.14778/3675034.3675036.

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As a fundamental task in graph data management, maximal clique enumeration (MCE) has attracted extensive attention from both academic and industrial communities due to its wide range of applications. However, MCE is very challenging as the number of maximal cliques may grow exponentially with the number of vertices. The state-of-the-art methods adopt a recursive paradigm to enumerate maximal cliques exhaustively, suffering from a large amount of redundant computation. In this paper, we propose a novel reduction-based framework for MCE, namely RMCE, that aims to reduce the search space and mini
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Das, Angsuman. "On nonzero component graph of vector spaces over finite fields." Journal of Algebra and Its Applications 16, no. 01 (2017): 1750007. http://dx.doi.org/10.1142/s0219498817500074.

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In this paper, we study nonzero component graph [Formula: see text] of a finite-dimensional vector space [Formula: see text] over a finite field [Formula: see text]. We show that the graph is Hamiltonian and not Eulerian. We also characterize the maximal cliques in [Formula: see text] and show that there exists two classes of maximal cliques in [Formula: see text]. We also find the exact clique number of [Formula: see text] for some particular cases. Moreover, we provide some results on size, edge-connectivity and chromatic number of [Formula: see text].
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Meghanathan, Natarajan. "Correlation Analysis between Maximal Clique Size and Centrality Metrics for Random Networks and Scale-Free Networks." Computer and Information Science 9, no. 2 (2016): 41. http://dx.doi.org/10.5539/cis.v9n2p41.

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<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The high-level contribution of this paper is a comprehensive analysis of the correlation levels between node centrality (a computationally light-weight metric) and maximal clique size (a computationally hard metric) in random network and scale-free network graphs generated respectively from the well-known Erdos-Renyi (ER) and Barabasi
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Conde-Cespedes, Patricia. "Approaching the Optimal Solution of the Maximal α-quasi-clique Local Community Problem". Electronics 9, № 9 (2020): 1438. http://dx.doi.org/10.3390/electronics9091438.

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Complex networks analysis (CNA) has attracted so much attention in the last few years. An interesting task in CNA complex network analysis is community detection. In this paper, we focus on Local Community Detection, which is the problem of detecting the community of a given node of interest in the whole network. Moreover, we study the problem of finding local communities of high density, known as α-quasi-cliques in graph theory (for high values of α in the interval ]0,1[). Unfortunately, the higher α is, the smaller the communities become. This led to the maximal α-quasi-clique community of a
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Baudin, Alexis, Clémence Magnien, and Lionel Tabourier. "Faster maximal clique enumeration in large real-world link streams." Journal of Graph Algorithms and Applications 28, no. 1 (2024): 149–78. http://dx.doi.org/10.7155/jgaa.v28i1.2932.

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Link streams offer a good model for representing interactions over time. They consist of links $(b,e,u,v)$, where $u$ and $v$ are vertices interacting during the whole time interval $[b,e]$. In this paper, we deal with the problem of enumerating maximal cliques in link streams. A clique is a pair $(C,[t_0,t_1])$, where $C$ is a set of vertices that all interact pairwise during the full interval $[t_0,t_1]$. It is maximal when neither its set of vertices nor its time interval can be increased. Some main works solving this problem are based on the famous Bron-Kerbosch algorithm for enumerating m
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Zhou, Yi, Jingwei Xu, Zhenyu Guo, Mingyu Xiao, and Yan Jin. "Enumerating Maximal k-Plexes with Worst-Case Time Guarantee." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2442–49. http://dx.doi.org/10.1609/aaai.v34i03.5625.

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The problem of enumerating all maximal cliques in a graph is a key primitive in a variety of real-world applications such as community detection and so on. However, in practice, communities are rarely formed as cliques due to data noise. Hence, k-plex, a subgraph in which any vertex is adjacent to all but at most k vertices, is introduced as a relaxation of clique. In this paper, we investigate the problem of enumerating all maximal k-plexes and present FaPlexen, an enumeration algorithm which integrates the “pivot” heuristic and new branching schemes. To our best knowledge, for the first time
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Dissertations / Theses on the topic "Maximal Clique Size"

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"Coloring Graphs from Almost Maximum Degree Sized Palettes." Doctoral diss., 2013. http://hdl.handle.net/2286/R.I.17753.

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abstract: Every graph can be colored with one more color than its maximum degree. A well-known theorem of Brooks gives the precise conditions under which a graph can be colored with maximum degree colors. It is natural to ask for the required conditions on a graph to color with one less color than the maximum degree; in 1977 Borodin and Kostochka conjectured a solution for graphs with maximum degree at least 9: as long as the graph doesn't contain a maximum-degree-sized clique, it can be colored with one fewer than the maximum degree colors. This study attacks the conjecture on multiple fronts
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Book chapters on the topic "Maximal Clique Size"

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Meghanathan, Natarajan. "Maximal Clique Size Versus Centrality: A Correlation Analysis for Complex Real-World Network Graphs." In Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2529-4_9.

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Schiff, Krzysztof. "Ant Colony Optimization Algorithm for Finding the Maximum Number of d-Size Cliques in a Graph with Not All m Edges between Its d Parts." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37720-4_23.

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Meghanathan, Natarajan. "Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs." In Advances in Computer and Electrical Engineering. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7598-6.ch087.

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The authors present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. They consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC), and betweenness centrality (BWC). They define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The authors observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. They observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).
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Meghanathan, Natarajan. "Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs." In Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch565.

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We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).
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Conference papers on the topic "Maximal Clique Size"

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Vandierendonck, Hans. "Differentiating Set Intersections in Maximal Clique Enumeration by Function and Subproblem Size." In ICS '24: 2024 International Conference on Supercomputing. ACM, 2024. http://dx.doi.org/10.1145/3650200.3656607.

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Borovina, Nihad, and Sead Kreso. "Approximating Size of Maximal Clique in a Node's Neighbourhood in the Ad Hoc Networks." In 2009 IEEE Wireless Communications and Networking Conference. IEEE, 2009. http://dx.doi.org/10.1109/wcnc.2009.4917601.

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Meghanathan, Natarajan. "On the Distribution of the Maximal Clique Size for the Vertices in Real-World Network Graphs and Correlation Studies." In International Conference on Computer Science and Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2015. http://dx.doi.org/10.5121/csit.2015.50901.

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Derici, Ilteris Murat, and Mihai Tudor Panu. "Determining the maximum clique size in large random geometric graphs." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999818.

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