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Balanced Clique Computation in Signed Networks: Concepts and Algorithms [article]

Zi Chen, Long Yuan, Xuemin Lin, Lu Qin, Wenjie Zhang
2022 arXiv   pre-print
Following the balanced clique model, we study the maximal balanced clique enumeration problem (MBCE) which computes all the maximal balanced cliques in a given signed network and the maximum balanced clique  ...  Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks.  ...  In this paper, we aim to devise efficient algorithms to enumerate all maximal balanced cliques in a given signed network.  ... 
arXiv:2204.00515v1 fatcat:hurjp4uf5fb6fixzkvtmwpzdfu

Maximal Multipolarized Cliques Search in Signed Networks

Jie Gao, Fei Hao, Geyong Min, Zhipeng Cai
2021 Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval  
This paper formulates the problem of Maximal Multipolarized Cliques Search (MMCS) in signed networks which is proved to be NP-hard.  ...  To address this problem, we first devise powerful pruning rules to reduce the signed network significantly and further develop an efficient algorithm to search all maximal multipolarized cliques in the  ...  ACKNOWLEDGMENTS This work was funded in part by the National Natural Science Foundation of China (Grant No. 61702317), the Fundamental Research Funds for the Central Universities (GK202103080), and the  ... 
doi:10.1145/3404835.3463014 fatcat:c75ggjzghjec3kfyimdum52tnq

Efficient Maximal Balanced Clique Enumeration in Signed Networks

Zi Chen, Long Yuan, Xuemin Lin, Lu Qin, Jianye Yang
2020 Proceedings of The Web Conference 2020  
problem which computes all the maximal balanced cliques in a given signed network.  ...  Clique is one of the most fundamental models for cohesive subgraph mining in network analysis. Existing clique model mainly focuses on unsigned networks.  ...  In this paper, we aim to devise efficient algorithms to enumerate all maximal balanced cliques in a given signed network. Applications.  ... 
doi:10.1145/3366423.3380119 dblp:conf/www/ChenY0QY20 fatcat:eexlfhgglrbufhdrjt6wxmhb7i

Effective and Efficient Core Computation in Signed Networks [article]

Junghoon Kim, Sungsu Lim, Jungeun Kim
2022 arXiv   pre-print
In this paper, we formulate (p,n)-core in signed networks by extending k-core. (p,n)-core simultaneously guarantees sufficient internal positive edges and deficient internal negative edges.  ...  Hence, we propose three efficient and effective algorithms to find a solution. Using real-world and synthetic networks, we demonstrate the superiority of our proposed algorithms.  ...  In Section 4, we present the result of the community search by utilising our problem. In summary, we focus on the 𝑘-core problem in signed networks by unifying two key concepts.  ... 
arXiv:2203.13712v2 fatcat:hxw4wofdprc4vfjml3vafyhdxm

Mining direct antagonistic communities in signed social networks

David Lo, Didi Surian, Philips Kokoh Prasetyo, Kuan Zhang, Ee-Peng Lim
2013 Information Processing & Management  
In a large signed network, the number of such combinations is very large.  ...  An extended discussion of potential applications of DACs is provided in Section 6. Identifying all direct antagonistic communities from a large signed network however is challenging.  ...  We propose a new problem of mining antagonistic communities from signed social networks. 2 2.  ... 
doi:10.1016/j.ipm.2012.12.009 fatcat:sci4ugch7fax7fb6obz7djpjre

A Novel Community Detection Method of Social Networks for the Well-Being of Urban Public Spaces

Yixuan Yang, Sony Peng, Doo-Soon Park, Fei Hao, Hyejung Lee
2022 Land  
In this article, we focus on proposing a method for detecting communities of signed social networks and mining -Quasi-Cliques for closely related users within them.  ...  We fully consider the relationship between friends and enemies of objects in signed networks, consider the mutual influence between friends or enemies, and propose a novel method to recompute the weighted  ...  In the future, we will consider improving the efficiency of our algorithm for the dynamic signed social network. Figure 1 . 1 Figure 1.  ... 
doi:10.3390/land11050716 fatcat:34mlwek47fg4nagd75qq55q44y

Structural Balance of Social Internet of Things Networks with Ambiguous Relationships

Hongfei Zhang, Li Zhu, Haifeng Du, Li Zhang, Kaiqi Zhang, Yutian Yan, Chaobo Wang, Wei Wang
2021 Wireless Communications and Mobile Computing  
Experiments show that our approach is more effective and efficient than former studies in optimizing dynamical evolution of structural balance in signed networks of SIoT.  ...  One of the factors that determines the effect and efficiency of service matching in SIoT is the structure of social networks.  ...  That is, the structure of a signed network is in equilibrium, when any edges in it is positive, or all the nodes can be partitioned into two cliques where nodes in the identical clique can be seen as friends  ... 
doi:10.1155/2021/7964409 fatcat:62d5nahb7rfxxmgvprivw7damu

A branch-and-cut algorithm for the maximum k-balanced subgraph of a signed graph

Rosa Figueiredo, Yuri Frota, Martine Labbé
2019 Discrete Applied Mathematics  
The particular case k = 2 yields the problem of finding a maximum balanced subgraph in a signed graph and its exact solution has been addressed before in the literature.  ...  We are interested in the solution of the maximum k-balanced subgraph problem. Let G = (V, E, s) be a signed graph and k a positive scalar.  ...  Acknowledgements This research benefited from the support of the "FMJH Program Gaspard Monge in optimization and operation research" (Project 2015-2842H), and from the support to this program from EDF.  ... 
doi:10.1016/j.dam.2018.11.022 fatcat:gbxmcyl5szha3dvbnwkrv5bx4u

Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks [article]

Saurabh Agrawal, Michael Steinbach, Daniel Boley, Snigdhansu Chatterjee, Gowtham Atluri, Anh The Dang, Stefan Liess, Vipin Kumar
2018 arXiv   pre-print
Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach.  ...  We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network.  ...  We proposed a novel and computationally efficient correlation network-based approach to find multipoles in the data.  ... 
arXiv:1810.02950v1 fatcat:2kcywheo6rawxh2fkbb6jtwhia

Page 6627 of Mathematical Reviews Vol. , Issue 99j [page]

1999 Mathematical Reviews  
Summary: “Signed graphs were introduced by F. Harary and have been studied by Harary, Kabell and Cartwright. A signed graph is said to be balanced if every cycle in the graph is positive.  ...  We propose and implement a genetic algo- rithm (GA) to solve the minimal spanning tree problem with a permutation ordering of its edges in a computationally efficient manner.  ... 

On detecting maximal quasi antagonistic communities in signed graphs

Ming Gao, Ee-Peng Lim, David Lo, Philips Kokoh Prasetyo
2015 Data mining and knowledge discovery  
We have conducted extensive experiments using synthetic signed graphs and two real networks to demonstrate the efficiency and accuracy of the Mascot  ...  Many networks can be modeled as signed graphs. These include social networks, and relationships/interactions networks.  ...  We address the MQACs detection problem using two main ideas, namely: (a) efficient pruning of search space, and (b) efficient enumeration of MQAC candidates.  ... 
doi:10.1007/s10618-015-0405-2 fatcat:7xq5tehfdrf7laydpvgwfy5q5m

Mining direct antagonistic communities in explicit trust networks

David Lo, Didi Surian, Kuan Zhang, Ee-Peng Lim
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
We develop an efficient algorithm that could analyze large trust networks leveraging the unique property of direct antagonistic community.  ...  In this work, we are interested in extracting direct antagonistic communities (DACs) within a rich trust network involving trusts and distrusts.  ...  Dorean [8] studied how to partition signed social network. Yang et al. [14] mined communities in signed network using a heuristic clustering approach.  ... 
doi:10.1145/2063576.2063722 dblp:conf/cikm/LoSZL11 fatcat:2d66k6e3sbfljj6lboyflf2vp4

Survey on Parallel Algorithms

Shweta S. Bagul
2012 IOSR Journal of Engineering  
In this talk, we will present various applications of parallel algorithms, challenges associated in designing them.  ...  The scalable performance and lower cost of parallel platforms is reflected in the wide variety of applications.  ...  all maximal cliques in a complex network  ... 
doi:10.9790/3021-0202338342 fatcat:ns4gvddvkzdi7pai6x7o7vlmvy

Mining maximal cliques from a large graph using MapReduce: Tackling highly uneven subproblem sizes

Michael Svendsen, Arko Provo Mukherjee, Srikanta Tirthapura
2015 Journal of Parallel and Distributed Computing  
, social networks, and biological networks.  ...  A maximal clique is perhaps the most fundamental dense substructure in a graph, and MCE is an important tool to discover densely connected subgraphs, with numerous applications to data mining on web graphs  ...  Acknowledgments This work was funded in part by the National Science Foundation through grants 0834743 and 0831903, and through a gift from Northrop Grumman Corporation.  ... 
doi:10.1016/j.jpdc.2014.08.011 fatcat:zdjxdmwr6jf7fn3detaxkgfv3e

Evaluating balancing on social networks through the efficient solution of correlation clustering problems

Mario Levorato, Rosa Figueiredo, Yuri Frota, Lúcia Drummond
2017 EURO Journal on Computational Optimization  
The solution of clustering problems defined on signed graphs can be used as a criterion to measure the degree of balance in social networks and this measure can be obtained with the optimal solution of  ...  One challenge for social network researchers is to evaluate balance in a social network.  ...  signed social networks.  ... 
doi:10.1007/s13675-017-0082-6 fatcat:ujmbnkx7rvcp7egw3z5lqfivqm
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