Download Analyzing Social Media Networks with Node: XL. Insights from by Derek L. Hansen, Ben Shneiderman and Marc A. Smith (Auth.) PDF

By Derek L. Hansen, Ben Shneiderman and Marc A. Smith (Auth.)

Businesses, marketers, members, and executive organizations alike want to social community research (SNA) instruments for perception into traits, connections, and fluctuations in social media. Microsoft's NodeXL is a unfastened, open-source SNA plug-in to be used with Excel. It offers quick graphical illustration of relationships of advanced networked information. however it is going extra than different SNA instruments -- NodeXL was once constructed through a multidisciplinary crew of specialists that collect details reviews, machine technological know-how, sociology, human-computer interplay, and over two decades of visible analytic concept and knowledge visualization right into a basic device a person can use. This makes NodeXL of curiosity not just to end-users but in addition to researchers and scholars learning visible and community analytics and their program within the genuine world.

In Analyzing Social Media Networks with NodeXL, individuals of the NodeXL improvement group as much as supply readers with an intensive and functional consultant for utilizing the device whereas additionally explaining the advance at the back of every one characteristic. mixing the theoretical with the sensible, this booklet applies particular SNA directions on to NodeXL, however the concept at the back of the implementation should be utilized to any SNA.

To study extra approximately studying Social Media Networks and NodeXL, stopover at the companion site at www.mkp.com/nodexl

*Walks you thru NodeXL, whereas explaining the idea and improvement in the back of each one step, offering takeaways that may practice to any SNA

*Demonstrates how visible analytics examine could be utilized to SNA instruments for the mass industry

*Includes case stories from researchers who use NodeXL on well known networks like e-mail, fb, Twitter, and wikis

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Digital Habitats; stewarding technology for communities. Portland, OR: CPsquare. I. 1 Introduction are primordial. Simply stated, a network is a collection of things and their relationships to one another. The “things” that are connected are called nodes, vertices, entities, and in some contexts people. The connections between the vertices are called edges, ties, and links. Many natural and artificial systems form networks, which exist in systems from the atomic level to the planetary level. Social networks are created whenever people interact, directly or indirectly, with other people, Human beings have been part of social networks since our earliest days.

However, many social network analysis metrics (discussed later) are designed for unweighted networks. Fortunately, any weighted network can be converted to an unweighted one by choosing a cutoff point. For example, an unweighted edge could be shown between individuals who exchanged at least 10 email messages, with no edge between people who exchanged fewer than 10 messages. 4 Network Data Representations Because network data differ from attribute data, there are different ways of representing it.

3] B. Shneiderman, C. , Addison-Wesley Publ. , Reading, MA, 2010. [4] E. Hall, The Silent Language, Doubleday Press, New York, 1990. [5] M. Smith, S. Farnham, S. Drucker, The Social Life of Small Graphical Chat Spaces, Proceeding ACM CHI 2000 Conference, The Hague, Netherlands, March 2000, New York: ACM Press, 2000. [6] D. Powazek, Chapter 8: Barriers to Entry  Making Them Work for It, In Design for Community, Waite Group Press, 2001. [7] E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action, Cambridge University Press, New York, 1990.

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