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November 11 2011

Top Stories: November 7-11, 2011

Here's a look at the top stories published across O'Reilly sites this week.

Thoughts on ebooks
Tim O'Reilly: "Our original ebook vision was of a world in which ebooks would be published in standard formats and could be read on any device, and where dominance of a particular piece of software or a particular e-reading device would not lock people in."


Confessions of a not-so-public speaker
Stepping out of our comfort zones and into the spotlight at events (and encouraging others to do likewise) can help address the perception that the tech community is solely populated by young white guys.

Social network analysis isn't just for social networks
The scientific methodology of social network analysis (SNA) helps explain not just how people connect, but why they come together as well. Here, "Social Network Analysis for Startups" co-author Maksim Tsvetovat offers a primer on SNA.

Access or ownership: Which will be the default?
Business, media, publishing, data, education — these are all areas where access versus ownership has organically popped up in Radar's coverage. But which model will win out in the long term?

Three game characteristics that can be applied to education
Cloud technologies and thoughtful roadmapping of digital technology can ensure that authenticity, social interaction, and play remain central components of education.


Tools of Change for Publishing, being held February 13-15 in New York, is where the publishing and tech industries converge. Register to attend TOC 2012.

November 09 2011

Social network analysis isn't just for social networks

Social networking has become a pervasive part of our everyday online experience, and by extension, that means the analysis and application of social data is an essential component of business.

In the following interview, "Social Network Analysis for Startups" co-author Maksim Tsvetovat (@maksim2042) offers a primer on social network analysis (SNA) and how it has relevance beyond social-networking services.

What is social network analysis (SNA)?

Maksim Tsvetovat: Social network analysis is an offshoot of the social sciences — sociology, political science, psychology, anthropology and others — that studies human interactions by using graph-theoretic approaches rather then traditional statistics. It's a scientific methodology for data analysis and also a collection of theories about how and why people interact — and how these interaction patterns change and affect our lives as individuals or societies. The theories come from a variety of social sciences, but they are always backed up with mathematical ways of measuring if a specific theory is applicable to a specific set of data.

In the science world, the field is considered interdisciplinary, so gatherings draw mathematicians, physicists, computer scientists, sociologists, political scientists and even an occasional rock musician.

As far as the technology aspect goes, the analysis methods are embodied in a set of software tools, such as the Python-based NetworkX library, which the book uses extensively. These tools can be used for analyzing and visualizing network data in a variety of contexts, from visualizing the spread of disease to business intelligence applications.

In terms of marketing applications, there's plenty of science behind "why things go viral" — and the book goes briefly into it — but I find that it's best to leave marketing to marketing professionals.

Does SNA refer specifically to the major social-networking services, or does it also apply beyond them?

Maksim Tsvetovat: SNA refers to the study of relationships between people, companies, organizations, websites, etc. If we have a set of relationships that may be forming a meaningful pattern, we can use SNA methods to make sense of it.

Major social-networking services are a great source of data for SNA, and they present some very interesting questions — most recently, how can a social network act as an early warning system for natural disasters? I'm also intrigued by the emergent role of Twitter as a "common carrier" and aggregation technology for data from other media. However, the analysis methodology is applicable to many other data sources. In fact, I purposefully avoided using Twitter as a data source in the book — it's the obvious place to start and also a good place to get tunnel vision about the technology.

Instead, I concentrated on getting and analyzing data from other sources, including campaign finance, startup company funding rounds, international treaties, etc., to demonstrate the potential breadth of applications of this technology.

Social Network Analysis for Startups — Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you'll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks.

Today Only Get "Social Network Analysis for Startups" for $9.99 (save 50%).

How does SNA relate to startups?

Maksim Tsvetovat: A lot of startups these days talk about social-this and social-that — and all of their activity can be measured and understood using SNA metrics. Being able to integrate SNA into their internal business intelligence toolkits should make businesses more attuned to their audiences.

I have personally worked with three startups that used SNA to fine-tune their social media targeting strategies by locating individuals and communities, and addressing them directly. Also, my methodologies have been used by a few large firms: the digital marketing agency DIGITAS is using SNA daily for a variety of high-profile clients. (Disclosure: my startup firm, DeepMile Networks, is involved in supplying SNA tools and services to DIGITAS and a number of others.)

What SNA shifts should developers watch for in the near future?

Maksim Tsvetovat: Multi-mode network analysis, which is analyzing networks with many types of "actors" (people, organizations, resources, governments, etc.). I approach the topic briefly in the book — but much remains to be done.

Also, watch for more real-time analysis. Most SNA is done on snapshot-style data that is, at best, a few hours out-of-date — some is years out-of-date. The release of Twitter's Storm tool should spur developers to make more SNA tools work on real-time and flowing data.

This interview was edited and condensed.

Associated photo on home and category pages: bulletin board [before there was twitter] by woodleywonderworks, on Flickr.

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