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Data mining for social network analysis

Jaideep Srivastava
2008 2008 IEEE International Conference on Intelligence and Security Informatics  
for sampling a small representative graph from a large graph -Strategies: Random Node, Random Edge, Random Degree Node, Random Edge Node, Random Walk etc.  ...  represent probabilistic distributions over link structures -Apply resulting model to predict link structure Link Mining (Getoor & Diehl, 2005) • Link Mining: Data Mining techniques that take into account  ... 
doi:10.1109/isi.2008.4565015 dblp:conf/isi/Srivastava08 fatcat:s746w6x6mngg7nx627sb5br2vq

Anonymizing Social Networks [chapter]

2010 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data.  ...  This includes a model of adversary knowledge, for which we consider several variants and make connections to known graph theoretical results.  ...  It has been widely applied to organizational networks to classify the influence or popularity of individuals and to detect collusion and fraud.  ... 
doi:10.1201/9781420091502-c15 fatcat:52cze3ar5rfh7iyzgy7gkmu6oy

Privacy in Social Networks

Elena Zheleva, Evimaria Terzi, Lise Getoor
2012 Synthesis Lectures on Data Mining and Knowledge Discovery  
Acknowledgments The authors would like to thank Michael Hay and Ashwin Machanavajjhala for the invaluable and thorough feedback on this manuscript.  ...  We would also like to thank the LINQS group at the University of Maryland, College Park and the data-management group at Boston University.  ...  [13] consider attacks for sensitive social link disclosure in social and affiliation networks, to which they refer as rich interaction graphs.  ... 
doi:10.2200/s00408ed1v01y201203dmk004 fatcat:x2zivcq7fjakbkkdayab5nuu2i

Social Network Analysis and Mining for Business Applications

Francesco Bonchi, Carlos Castillo, Aristides Gionis, Alejandro Jaimes
2011 ACM Transactions on Intelligent Systems and Technology  
Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of  ...  The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining  ...  ACKNOWLEDGMENTS The authors would like to thank the reviewers for their feedback.  ... 
doi:10.1145/1961189.1961194 fatcat:rstwktyuzze6fgutld3g36n2im

Special Issue on Searching and Mining the Web and Social Networks

Nelly Litvak, Sebastiano Vigna
2014 Internet Mathematics  
This issue of Internet Mathematics, titled "Searching and Mining the Web and Social Networks," was born out of the interest of the editors in the problem of searching and analyzing not only the web, but  ...  also social networks in a broad sense.  ...  degrees of neighboring nodes in general social networks, and in common random graph models of them.  ... 
doi:10.1080/15427951.2014.916132 fatcat:5zurvowrevf6tp26s43umhp5kq

Robust active attacks on social graphs

Sjouke Mauw, Yunior Ramírez-Cruz, Rolando Trujillo-Rasua
2019 Data mining and knowledge discovery  
In order to support this claim, we develop the notion of a robust active attack, which is an active attack that is resilient to small perturbations of the social network graph.  ...  In order to prevent the disclosure of privacy-sensitive data, such as names and relations between users, social network graphs have to be anonymised before publication.  ...  the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s10618-019-00631-5 fatcat:uiwc5ikddzeozjyhc5jcknggvq

Privacy Risk in Graph Stream Publishing for Social Network Data

Nigel Medforth, Ke Wang
2011 2011 IEEE 11th International Conference on Data Mining  
To understand how social networks evolve over time, graphs representing the networks need to be published periodically or on-demand.  ...  One of our contributions is a formal method to assess the privacy risk of this type of attacks and empirically study the severity on real social network data.  ...  attack, assuming that the published graphs are anonymized by the stable link randomization. 4) We study the severity of the identified attack on real life social network data.  ... 
doi:10.1109/icdm.2011.120 dblp:conf/icdm/MedforthW11 fatcat:s5v7rrakjbbefmmyqxkpwj4v7u

Social Sensing [chapter]

Charu C. Aggarwal, Tarek Abdelzaher
2012 Managing and Mining Sensor Data  
This requires the development of trajectory mining techniques, which can mine the GPS data for interesting social patterns.  ...  It also leads to a number of challenges, since such data may often be private, and it is important to be able to perform the mining process without violating the privacy of the users.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1007/978-1-4614-6309-2_9 fatcat:7k74wrhmozearbfbfod3yqjxcm

Prying Data out of a Social Network

Joseph Bonneau, Jonathan Anderson, George Danezis
2009 2009 International Conference on Advances in Social Network Analysis and Mining  
We examine the difficulty of collecting profile and graph information from the popular social networking website Facebook and report two major findings.  ...  Preventing adversaries from compiling significant amounts of user data is a major challenge for social network operators.  ...  Graph: A broader threat is extraction of the social graph, that is, the graph consisting of users (vertices) and their friendship links (edges).  ... 
doi:10.1109/asonam.2009.45 dblp:conf/asunam/BonneauAD09 fatcat:ez7sdwexuzdwjfw7vpelmsqeqa

SNAKDD 2008 social network mining and analysis postworkshop report

Haizheng Zhang, Marc Smith, C. Lee Giles, John Yen, Henry Foley
2008 SIGKDD Explorations  
In this report, we summarize the contents and outcomes of the recent SNAKDD 2008 workshop on Social Network Mining and Analysis that was held in conjunction with the 14th  ...  We extend our thanks and encouragements to the entire Social network analysis community, including all those who attended the workshop and contributed in the interesting discussions.  ...  We would also like to express our gratitude to the members of the Program Committee for their vigilant and timely reviews, namely: Lada Adamic, Aris Anagnostopoulos, Arindam Banerjee, Tanya Berger-Wolf  ... 
doi:10.1145/1540276.1540298 fatcat:7keuj6va3jczxelpmarmamep2u

A survey of data mining and social network analysis based anomaly detection techniques

Ravneet Kaur, Sarbjeet Singh
2016 Egyptian Informatics Journal  
The paper presents a review of number of data mining approaches used to detect anomalies.  ...  A special reference is made to the analysis of social network centric anomaly detection techniques which are broadly classified as behavior based, structure based and spectral based.  ...  [17] proposed a generic approach for detection of attacks, more specifically mentioned as Random Link Attacks (RLAs).  ... 
doi:10.1016/j.eij.2015.11.004 fatcat:jixqyc6p5vfx5kkiczcwtt32fy

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.  ...  This book is an accessible introduction to the study of community detection and mining in social media.  ...  The members of the Social Computing Group, Data Mining and Machine Learning Lab at Arizona State University made this project enjoyable.  ... 
doi:10.2200/s00298ed1v01y201009dmk003 fatcat:bxcd7hnfffdadgg6zx6mgiqloy

A Novel Approach for Secure Hidden Community Mining in Social Networks using Data Mining Techniques

R. RenugaDevi, M. Hemalatha
2014 International Journal of Computer Applications  
The security issue such as a Sybil attack (Multiple fake Identities attack) arises in these network structures.  ...  Sometimes it refers to the special kind of network arrangement where the Community Mining discovers all communities hidden in distributed networks based on their important similarities.  ...  Community mining problem is called as sub graph identification [9] . First community mining and detection research started with the homogeneous social networks.  ... 
doi:10.5120/15219-3728 fatcat:pembuqybrnetjhgp5fdy4vvqr4

Intrusion as (anti)social communication

Qi Ding, Natallia Katenka, Paul Barford, Eric Kolaczyk, Mark Crovella
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
Anomaly-based network attack detection offers a compelling alternative to signature-based methods.  ...  This inherent challenge speaks directly to the need for complementary methods for network-based attack detection.  ...  Acknowledgements We are grateful to Johannes Ullrich and the SANS Institute (www. sans.org) for making DShield logs available to us for the purposes of this study.  ... 
doi:10.1145/2339530.2339670 dblp:conf/kdd/DingKBKC12 fatcat:lswg3324e5fe3j4ag46nrylhlq

Data Mining in Social Networks and its Application in Counterterrorism

2019 International journal of recent technology and engineering  
Investigative Data Mining is used for this which is defined as when Social Network Analysis (SNA) is applied to Terrorist Networks to gather useful insights about the network..  ...  Social Networks are best represented as complex interconnected graphs. Graph theory analysis can hence be used for insight into various aspects of these complex social networks.  ...  Graph representation of SNA topologies are used for various purposes such as community detection, network structures, random walks and temporal networks.  ... 
doi:10.35940/ijrte.b2333.098319 fatcat:rtxeou7lwvdvnoifargejaufja
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