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Graph Mining and Social Network Analysis

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.

Graph theory – Wikipedia

Graph theory – Wikipedia

Graph theory is also widely used in sociology as a way, for example, to measure actors’ prestige or to explore rumor spreading, notably through the use of social network analysis software.

This post presents an example of social network analysis with R using package igraph. The data to analyze is Twitter text data of @RDataMining used in the example of Text Mining, and it can be downloaded as file “termDocMatrix.rdata” at the Data webpage. Putting it in a general scenario of

Analysts recognize Social Media Networks as virtual treasure troves of information for data mining, and information for sensing public opinion trends.

Social Network Analysis for Startups: Finding connections on the social web: 9781449306465: Computer Science Books @ Amazon.com

SNAP for C++: Stanford Network Analysis Platform. Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.It is written in C++ and easily scales to massive networks with hundreds of millions of nodes, and billions of edges.

Social Network Analysis (SNA) tools hold the potential to help tax and revenue Agencies identify non-compliance and tax fraud.

Mining Graph Data [Diane J. Cook, Lawrence B. Holder] on Amazon.com. *FREE* shipping on qualifying offers. This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided.

What is Twitter, a Social Network or a News Media? Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon Proceedings of the 19th International World Wide Web (WWW) Conference, April 26-30, 2010, Raleigh NC (USA)

What is Text Analysis, Text Mining, Text Analytics. Text Analytics is the process of converting unstructured text data into meaningful data for analysis, to measure customer opinions, product reviews, feedback, to provide search facility, sentimental analysis and entity modeling to support fact based decision making.

Top 31 Graph Databases : A graph database is based on graph theory, uses nodes, properties, and edges and provides index-free adjacency. These database uses graph structures with nodes, edges, and properties to represent and store data.

Datasets – RDataMining.com: R and Data Mining

Datasets – RDataMining.com: R and Data Mining

termDocMatrix.rdata Download: A term-document matrix of @RDataMining Tweets. It can be used for text mining and also for social network analysis.

In my post on how to become a niche rockstar I said that in order to be at the top of your industry (in a lot of cases online) it’s important to know as much

Bibliography of Research on Social Network Sites. Aaltonen, S,, Kakderi, C,, Hausmann, V, and Heinze, A. (2013). Social media in

Top Data Mining Resources: 50 Tutorials, Articles and Videos to Learn Data Mining Methods, Analysis and More

What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?

The following pages describe over 300 datasets that are available for this course. All data, except for Appleby’s Red Deer data set, are coded in the UCINET DL format.

The GDELT Project is a realtime network diagram and database of global human society for open research

Maltego is an interactive data mining tool that renders directed graphs for link analysis. The tool is used in online investigations for finding relationships between pieces of information from various sources located on the Internet.

The Digital Humanities Summer Institute at the University of Victoria provides an ideal environment for discussing and learning about new computing technologies and how they are influencing teaching, research, dissemination, and

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