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Cliques are Too Strict for Representing Communities: Finding Large k-plexes in Real Networks

Alessio Conte, Donatella Firmani, Caterina Mordente, Maurizio Patrignani, Riccardo Torlone
2018 Sistemi Evoluti per Basi di Dati  
k-plexes are a formal yet flexible way of defining communities in networks.  ...  They generalize the notion of cliques and are more appropriate in most real cases: while a node of a clique C is connected to all other nodes of C, a node of a k-plex may miss up to k connections.  ...  So strict, in fact, that cliques are generally thought to be too rigid to be used in practice [17] .  ... 
dblp:conf/sebd/ConteFMPT18 fatcat:2ufwo64defcotkqd35ci2xdfpy

Detecting Communities in Networks by Merging Cliques [article]

Bowen Yan, Steve Gregory
2012 arXiv   pre-print
Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks.  ...  We propose a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity.  ...  First, most communities are not cliques: the requirement that every pair of vertices be connected is too strict in practice.  ... 
arXiv:1202.0480v1 fatcat:uyg56o6hxfchjdwi5pgieq7h2u

A Survey of Community Search Over Big Graphs [article]

Yixiang Fang, Xin Huang, Lu Qin, Ying Zhang, Wenjie Zhang, Reynold Cheng, Xuemin Lin
2019 arXiv   pre-print
Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time.  ...  An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally.  ...  Acknowledgments We would like to thank Jiafeng Hu *For lack of space, we use abbreviations for the names of major conferences and journals in database and data mining areas (e.g., we use "PVLDB" to mean  ... 
arXiv:1904.12539v2 fatcat:swx7eervgbbgxpcf6znkx6cne4

Community Structure in Graphs [article]

Santo Fortunato, Claudio Castellano
2007 arXiv   pre-print
Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs.  ...  Other complications are represented by the possible occurrence of hierarchies, i.e. communities which are nested inside larger communities, and by the existence of overlaps between communities, due to  ...  Examples of k-clique communities are shown in Fig. 10 .  ... 
arXiv:0712.2716v1 fatcat:pyocmmtamveilpjkjfsgxcasku

Community Structure in Graphs [chapter]

Santo Fortunato, Claudio Castellano
2009 Encyclopedia of Complexity and Systems Science  
Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs.  ...  Other complications are represented by the possible occurrence of hierarchies, i.e. communities which are nested inside larger communities, and by the existence of overlaps between communities, due to  ...  Examples of k-clique communities are shown in Fig. 10 .  ... 
doi:10.1007/978-0-387-30440-3_76 fatcat:hll7abxsfbem7nfuhfmfawla5u

A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network

Sanjiv Sharma, G. N. Purohit
2013 International Journal of Computer Applications  
The main goal is to provide a road map for researchers working on different measures for tracking communities in Social Network.  ...  In this paper a state of the art survey of the works done on community tracking in social network.  ...  Clique and k-plex analysis Cliques and k-plexes have been used to characterize groupings in social networks [29, 30, 31] .  ... 
doi:10.5120/10498-5261 fatcat:ptdhyeuxjjgwvoeqp4tpv4elza

A Tutorial on Clique Problems in Communications and Signal Processing [article]

Ahmed Douik, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
2020 arXiv   pre-print
To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and k-clique problems.  ...  The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple  ...  ACKNOWLEDGMENT The authors wish to thank Ahmed K. Sultan Salem for his helpful discussions and valuable comments.  ... 
arXiv:1808.07102v4 fatcat:ynmpnh5jsfdspdrtfv7wb5pm4a

Community detection in graphs

Santo Fortunato
2010 Physics reports  
Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs.  ...  One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same  ...  In order to find k-clique communities, one searches first for maximal cliques.  ... 
doi:10.1016/j.physrep.2009.11.002 fatcat:4ehi6spclncgtevcwxwztmhw4y

A classification for community discovery methods in complex networks

Michele Coscia, Fosca Giannotti, Dino Pedreschi
2011 Statistical analysis and data mining  
In the last few years many real-world networks have been found to show a so-called community structure organization.  ...  According to this definition it then extracts the communities that are able to reflect only some of the features of real communities.  ...  ACKNOWLEDGMENTS We gratefully acknowledge Sune Lehmann for useful discussions.  ... 
doi:10.1002/sam.10133 fatcat:vyy377nwdnc7pigfpiqfx7x3eq

A meta-algorithm for finding large k-plexes

Alessio Conte, Donatella Firmani, Maurizio Patrignani, Riccardo Torlone
2021 Knowledge and Information Systems  
However, it has been observed that cliques are too strict to represent communities in practice. The k-plex relaxes the notion of clique, by allowing each node to miss up to k connections.  ...  Although k-plexes are more flexible than cliques, finding them is more challenging as their number is greater. In addition, most of them are small and not significant.  ...  The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.  ... 
doi:10.1007/s10115-021-01570-8 fatcat:ktoddg3m5zfh7c555qujtcoliq

Fast Enumeration of Large k-Plexes

Alessio Conte, Donatella Firmani, Caterina Mordente, Maurizio Patrignani, Riccardo Torlone
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
k-plexes are a formal yet exible way of de ning communities in networks. ey generalize the notion of cliques and are more appropriate in most real cases: while a node of a clique C is connected to all  ...  In this paper we propose a new approach for enumerating large k-plexes in networks that speeds up the search by several orders of magnitude, leveraging on (i) methods for strongly reducing the search space  ...  ACKNOWLEDGMENTS We thank the authors of [5] for providing us with their code and for discussions on the subject of this paper.  ... 
doi:10.1145/3097983.3098031 dblp:conf/kdd/ConteFMPT17 fatcat:q6bjujltrjc77dv4mju2nule7e

Detecting social cliques for automated privacy control in online social networks

Hakan Yildiz, Christopher Kruegel
2012 2012 IEEE International Conference on Pervasive Computing and Communications Workshops  
Interestingly, concerns of social network users about these risks are related not only to adversarial activities but also to users they are directly connected to (friends).  ...  As a result, users frequently do not use these mechanisms, either due to a lack of concern about privacy, but more often due to the large amount of time required for the necessary setup and management.  ...  Scheme.: Complete subgraphs are often considered to be too strict to represent social cliques, as cliques display looser connections in reality.  ... 
doi:10.1109/percomw.2012.6197509 dblp:conf/percom/YildizK12 fatcat:w2f5h7kkivbizbjxi7onqjeiky

Community Detection and Mining in Social Media

Lei Tang, Huan Liu
2010 Synthesis Lectures on Data Mining and Knowledge Discovery  
In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media.  ...  The readers are encouraged to visit the book website for the latest information:  ...  We are grateful to Professor Sun-Ki Chai, Professor Michael Hechter, Dr. John Salerno and Dr. Jianping Zhang for many inspiring discussions.  ... 
doi:10.2200/s00298ed1v01y201009dmk003 fatcat:bxcd7hnfffdadgg6zx6mgiqloy

Community detection in Social Media

Symeon Papadopoulos, Yiannis Kompatsiaris, Athena Vakali, Ploutarchos Spyridonos
2011 Data mining and knowledge discovery  
Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics  ...  More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining  ...  Examples of such community definitions are cliques, n-cliques, n-clubs, n-clans, k-plexes, and k-cores (Wasserman and Faust 1994; Scott 2000) .  ... 
doi:10.1007/s10618-011-0224-z fatcat:uk72rldhrbagbll56hjnu6altq

Multivariate Algorithmics for Finding Cohesive Subnetworks

Christian Komusiewicz
2016 Algorithms  
We survey different models of cohesive graphs, commonly referred to as clique relaxations, that are used in the detection of network communities.  ...  For each clique relaxation, we give an overview of basic model properties and of the complexity of the problem of finding large cohesive subgraphs under this model.  ...  Acknowledgments: Christian Komusiewicz was supported by the DFG project "Multivariate Algorithmics for Graph and String Problems in Bioinformatics" (KO 3669/4-1).  ... 
doi:10.3390/a9010021 fatcat:hqtngvw5pze35blschz3zdvi7i
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