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Large scale cohesive subgraphs discovery for social network visual analysis

Feng Zhao, Anthony K. H. Tung
2012 Proceedings of the VLDB Endowment  
Graphs are widely used in large scale social network analysis nowadays.  ...  However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging.  ...  In this paper, we develop a novel social network visual analytic framework for large scale cohesive subgraphs discovery.  ... 
doi:10.14778/2535568.2448942 fatcat:bvdogxezzve2lnyziuefrff4ai

Narratives in the Network: Interactive Methods for Mining Cell Signaling Networks

M. Shahriar Hossain, Monika Akbar, Nicholas F. Polys
2012 Journal of Computational Biology  
Subgraph Discovery (FSG) approach by magnitudes.  ...  We formulate the problem of discovering pathway relationships as a subgraph discovery problem and propose a new technique called Subgraph-Extension Generation (SEG), which outperforms the traditional Frequent  ...  The authors acknowledge Advanced Research Computing at Virginia Tech (http://www.arc.vt.edu) for providing computational resources and technical support that have contributed to the results reported within  ... 
doi:10.1089/cmb.2011.0244 pmid:22897227 pmcid:PMC3440011 fatcat:rsixp2f77be5lolsq4didxq3di

CSV

Nan Wang, Srinivasan Parthasarathy, Kian-Lee Tan, Anthony K. H. Tung
2008 Proceedings of the 2008 ACM SIGMOD international conference on Management of data - SIGMOD '08  
In this article we propose an approximate algorithm, to mine and visualize cohesive subgraphs (dense sub components) within a large graph.  ...  Extracting dense sub-components from graphs efficiently is an important objective in a wide range of application domains ranging from social network analysis to biological network analysis, from the World  ...  Acknowledgment: We will like to thank Zhiping Zeng and Jianyong Wang for making the source code and datasets for CLAN available to us.  ... 
doi:10.1145/1376616.1376663 dblp:conf/sigmod/WangPTT08 fatcat:saa2scvzljh6porn5e2i2ppe5q

Analysis and Visualization of Dynamic Networks [chapter]

Faraz Zaidi, Chris Muelder, Arnaud Sallaberry
2014 Encyclopedia of Social Network Analysis and Mining  
Tools: Introduction, • 00268 -Visual Methods for Social Network Analysis, • 00044 -Visualization of Large Networks.  ...  We have listed below some of the most relevant titles: • 00223 -Community Evolution, • 00215 -Community Discovery and Analysis in Large-Scale Online/Offline Social Networks, • -Community Identification  ... 
doi:10.1007/978-1-4614-6170-8_382 fatcat:ccsnmkpgjnbgjofm3dtcedjgjm

Analysis and Visualization of Dynamic Networks [chapter]

Faraz Zaidi, Chris Muelder, Arnaud Sallaberry
2017 Encyclopedia of Social Network Analysis and Mining  
Tools: Introduction, • 00268 -Visual Methods for Social Network Analysis, • 00044 -Visualization of Large Networks.  ...  We have listed below some of the most relevant titles: • 00223 -Community Evolution, • 00215 -Community Discovery and Analysis in Large-Scale Online/Offline Social Networks, • -Community Identification  ... 
doi:10.1007/978-1-4614-7163-9_382-1 fatcat:wcnq6a5ncffh7baxftl43vs6eq

OCSM : Finding Overlapping Cohesive Subgraphs with Minimum Degree [article]

Junghoon Kim, Sungsu Lim, Jungeun Kim
2022 arXiv   pre-print
Cohesive subgraph discovery in a network is one of the fundamental problems and investigated for several decades.  ...  In this paper, we propose the Overlapping Cohesive Subgraphs with Minimum degree (OCSM) problem which combines three key concepts for OCSM : (i) edge-based overlapping, (ii) the minimum degree constraint  ...  [45] proposed (𝑘, 𝑟 )-core for an attributed social network.  ... 
arXiv:2202.03255v3 fatcat:l3tdtplxsfebbncasxygebziiy

A Survey of Algorithms for Dense Subgraph Discovery [chapter]

Victor E. Lee, Ning Ruan, Ruoming Jin, Charu Aggarwal
2010 Managing and Mining Graph Data  
In this chapter, we present a survey of algorithms for dense subgraph discovery.  ...  For example, the problem of clustering is largely concerned with that of finding a fixed partition in the data, whereas the problem of dense subgraph discovery defines these dense components in a much  ...  Dense component discovery and analysis is one important aspect of network analysis.  ... 
doi:10.1007/978-1-4419-6045-0_10 dblp:series/ads/LeeRJA10 fatcat:u5vzetjlovetpdcxdwjqihdjnq

A Query-Driven System for Discovering Interesting Subgraphs in Social Media [article]

Subhasis Dasgupta, Amarnath Gupta
2021 arXiv   pre-print
The technique combines the notion of a group-by operation on a graph and the notion of subjective interestingness, resulting in an automated discovery of interesting subgraphs.  ...  We present a discovery algorithm that first chooses a "background" graph based on a user's analytical interest and then automatically discovers subgraphs that are structurally and content-wise distinctly  ...  We also acknowledge SDSC cloud support, particularly Christine Kirkpatrick & Kevin Coakley, for generous help and support for collecting and managing our tweet collection.  ... 
arXiv:2102.09120v1 fatcat:wn2gl6krvzb3xlkvn7u3kqieaq

Analysis and Visualization of Dynamic Networks [article]

Faraz Zaidi, Chris Muelder, Arnaud Sallaberry
2014 arXiv   pre-print
This chapter provides an overview of the different techniques and methods that exist for the analysis and visualization of dynamic networks.  ...  From developing network generation models to developing temporal metrics and measures, from structural analysis to visual analysis, there is room for further exploration in almost every dimension where  ...  Tools: Introduction, • 00268 -Visual Methods for Social Network Analysis, • 00044 -Visualization of Large Networks.  ... 
arXiv:1409.5034v1 fatcat:edgnct432ffffej5qq6uhb2mcq

A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration

J Olamaei, A Arefi, A H Mazinan, T Niknam
2010 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE)  
Graph mining techniques are commonly used for knowledge discovery in social networks to unveil intricate structural patterns among users.  ...  In summary, this research provides a significant contribution to the research areas of social network analysis and mining, and frequent subgraph mining by proposing new types of knowledge, efficient methods  ...  Besides, there is a high computational cost in calculating these measures for each user in large scale social networks.  ... 
doi:10.1109/iccae.2010.5451699 fatcat:xtkq7vrlzvd33ft2wysg7q2jny

Familiarity-based Collaborative Team Recognition in Academic Social Networks [article]

Shuo Yu, Feng Xia, Chen Zhang, Kathleen Keogh, Honglong Chen
2022 arXiv   pre-print
Extensive experiments have been conducted upon a large-scale data set. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams.  ...  Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods.  ...  They analyzed the application of these measures in the discovery, counting, analysis, and clustering of the network motif.  ... 
arXiv:2204.02667v1 fatcat:bpdwyapm4zbirbizmmpm6lzxre

From Patterns in Data to Knowledge Discovery: What Data Mining Can Do

Francesco Gullo
2015 Physics Procedia  
Data mining is defined as the computational process of analyzing large amounts of data in order to extract patterns and useful information.  ...  In the last few decades, data mining has been widely recognized as a powerful yet versatile data-analysis tool in a variety of fields: information technology in primis, but also clinical medicine, sociology  ...  ., protein-interaction networks), chemical data analysis (e.g., chemical compounds), communication networking (e.g., device networks, road networks), social network analysis, Web link analysis, and so  ... 
doi:10.1016/j.phpro.2015.02.005 fatcat:dbtp3fk4ifdy3f3zv7wz6dbolm

People search and activity mining in large-scale community-contributed photos

Yan-Ying Chen
2012 Proceedings of the 20th ACM international conference on Multimedia - MM '12  
There arises a strong need for automatically analyzing the people shown in large-scale photos because these visual data comprise abundant consumer activities which greatly benefit demographic analysis  ...  Most importantly, this framework effectively relieves costly annotation efforts and ensures scalability for large-scale media.  ...  media for personalized recommendations and (4) social subgraph discovery for predicting social group types.  ... 
doi:10.1145/2393347.2396498 dblp:conf/mm/Chen12 fatcat:zhtpqh5zdbfwrpwfgswjzz5veq

Social influence analysis in large-scale networks

Jie Tang, Jimeng Sun, Chi Wang, Zi Yang
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
In large social networks, nodes (users, entities) are influenced by others for various reasons.  ...  To scale to real large networks, TAP is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework.  ...  Table 5 : Example of influence analysis from the coauthor data set.  ... 
doi:10.1145/1557019.1557108 dblp:conf/kdd/TangSWY09 fatcat:5niwiez3infqxajj4r3cdjssey

Multimedia Semantics: Interactions Between Content and Community

Hari Sundaram, Lexing Xie, Munmun De Choudhury, Yu-Ru Lin, Apostol Natsev
2012 Proceedings of the IEEE  
We also discuss two emerging issues related to the analysis of social networks-robust data sampling and scalable data analysis.  ...  Finally, we show how analysis of visual content-tracing content remixes, in particular-can help us understand the relationship amongst YouTube participants.  ...  For the first timevia social networks including Twitter-we are able to instrument social activity at a very large scale.  ... 
doi:10.1109/jproc.2012.2191529 fatcat:tayhtgkr6jfz7l2efpic4ygaqy
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