Vizster
Visualizing Online Social Networks

Jeffrey Heer – jheer@cs.berkeley.edu

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Vizster is an interactive visualization tool for online social networks, allowing exploration of the community structure of social networking services such as friendster.com, tribe.net , and orkut. Such services provide means by which users can publicly articulate their mutual "friendship" in the form of friendship links, forming an undirected graph in which users are the nodes and friendship links are the edges. These services also allow users to describe themselves in a profile, including attributes such as age, marital status, sexual orientation, and various interests. These services profess any number of goals, ranging from supporting dating and creating communities of shared interest to facilitating new business connections. Newman provides a wonderful overview of the statistical properties of such networks and boyd describes emergent social phenomena surrounding social networking services .

Vizster provides a visualization of such services, providing an interactive sociogram for exploring the links between network members. In addition to visualizing "friendship" linkages, Vizster supports a range of exploratory search features, providing visualization of the rich profile data characteristic of these services, features which traditional sociograms 3 are not designed to communicate. The current application visualizes a "snowball" sample of the popular friendster social networking service, encompassing over 1.5 million profiles and the linkages between them, roughly a quarter of active friendster users at the time of writing.

Vizster currently limits itself to ego-centric social networks, or network views centered on a single individual and her direct linkages. Though this prevents viewing of the higher-level structure of the network, this compromise allows the interface to maintain real-time interaction and animation while still providing insight into the community structure of the service. Additionally, both Milgram's classic small-world experiment 7 and a more recent investigation by Adamic et al. (1) argue that local cues are sufficient for efficient navigation of such networks.

1. Adamic, L. and E. Adar, How to Search a Social Network. Technical Report, Information Dynamics Laboratory, HP Labs, Palo Alto 2003.

7. Milgram, S., The Small World Problem. Psychology Today, 1967. 61.