Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques

In the recent decade, the Online Social Network (OSN) has gained remarkable attention. Accessing to OSN sites such as Twitter, Facebook, LinkedIn and Google Plus; the most dominant social media in the world, through the internet and the web 2.0 technologies has become more comfortable. These days through these online social networks, it becomes very easy for anyone to meet the people of the same interests for learning and sharing precious information. Online Social Network Analysis (OSNA) is an essential and important technique to understand the social structure, social relationships and social behaviors of OSN. OSNA deals with the interaction between individuals by considering them as nodes of a network whereas their relations are mapped as network edges. Now, it has increased various challenges for the evolution of the web and simultaneously increased the dynamic changes in its structure so it became harder to manually analyze very broad OSN. This survey investigates the current progression in the field of knowledge discovery in OSNA and covers all basic techniques of Data, Text, and Web mining that are widely used for the exploration of the unstructured and structured data available on the OSNA. The targets for OSNA are mainly focused on resources

[1]  Matthew Richardson,et al.  The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank , 2001, NIPS.

[2]  M. Grobelnik,et al.  Predicting content from hyperlinks , 1999 .

[3]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[4]  Edward A. Fox,et al.  Social media use by government: from the routine to the critical , 2011, dg.o '11.

[5]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[6]  P. Alli,et al.  An optimized approach to predict the stock market behavior and investment decision making using benchmark algorithms for Naïve investors , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[7]  Brij B. Gupta,et al.  Auditing Defense against XSS Worms in Online Social Network-Based Web Applications , 2016 .

[8]  Bettina Berendt,et al.  Using Site Semantics to Analyze, Visualize, and Support Navigation , 2004, Data Mining and Knowledge Discovery.

[9]  Myra Spiliopoulou,et al.  Data Mining for Measuring and Improving the Success of Web Sites , 2004, Data Mining and Knowledge Discovery.

[10]  Jonathan I. Maletic,et al.  Journal of Software Maintenance and Evolution: Research and Practice Survey a Survey and Taxonomy of Approaches for Mining Software Repositories in the Context of Software Evolution , 2022 .

[11]  Sophia Ananiadou,et al.  Highly scalable Text Mining - parallel tagging application , 2009, 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.

[12]  Taeho Jo SITE Categorizer ) : Neural Network for Text Categorization , 2007 .

[13]  Lacy Tite,et al.  Understanding Social Media , 2008 .

[14]  Steven M. Goodreau,et al.  Advances in exponential random graph (p*) models applied to a large social network , 2007, Soc. Networks.

[15]  Andreas Hotho,et al.  Conceptual User Tracking , 2003, AWIC.

[16]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[17]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[18]  E. Raju,et al.  Analysis of Social Networks Using the Techniques of Web Mining , 2012 .

[19]  Feng Li Extracting Structure of Web Site Based on Hyperlink Analysis , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[20]  Juan Li,et al.  Efficient Data Sharing over Large-Scale Distributed Communities , 2011, Intelligent Decision Systems in Large-Scale Distributed Environments.

[21]  Bamshad Mobasher,et al.  A Road Map to More Effective Web Personalization: Integrating Domain Knowledge with Web Usage Mining , 2003, International Conference on Internet Computing.

[22]  Borko Furht,et al.  Handbook of Social Network Technologies and Applications , 2010, Handbook of Social Network Technologies.

[23]  NassifAli Bou,et al.  Data mining techniques in social media , 2016 .

[24]  John Scott,et al.  Social Network Analysis, Overview of , 2009, Encyclopedia of Complexity and Systems Science.

[25]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[26]  Gang Wang,et al.  Research and Implement of Classification Algorithm on Web Text Mining , 2007, Third International Conference on Semantics, Knowledge and Grid (SKG 2007).

[27]  Rinkle Rani,et al.  A parallel fuzzy clustering algorithm for large graphs using Pregel , 2017, Expert Syst. Appl..

[28]  Mahmudur Rahman,et al.  Pattern Discovery of Web Usage Mining , 2009, 2009 International Conference on Computer Technology and Development.

[29]  L. Sorensen,et al.  User managed trust in social networking - Comparing Facebook, MySpace and Linkedin , 2009, 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology.

[30]  Thomas M. Lento The Ties that Blog: Examining the Relationship Between Social Ties and Continued Participation in the Wallop Weblogging System , 2006 .

[31]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[32]  Chaomei Chen,et al.  Mining the Web: Discovering knowledge from hypertext data , 2004, J. Assoc. Inf. Sci. Technol..