Help-seeking communities have been playing an increasingly critical role the way people seek and share information online, forming the basis for knowledge dissemination and accumulation. Consider:

• About.com, a popular help site (http://about.com), boasts 30 million distinct users each month
• Knowledge-iN, a Korean site (http://kin.naver.com/), has accumulated 1.5 million question and answers.

Many additional sites exist from online stock trading discussions to medical advice communities. These range from simple text-based newsgroups to intricate immersive virtual reality multiuser worlds.

Unfortunately, the very size of these communities may impede an individual’s ability to find
relevant answers or advice. Which replies were written by experts and which by novices? As these help-seeking communities are also often primitive technically, they often cannot help the user distinguish between e.g. expert and novice advice. We would therefore like to find mechanisms to augment their functionality and social life. Research is proceeding to make use of the available structure in online communities to design new systems and algorithms. These are largely focused on social network characteristics of these communities.

However, differing network structures and dynamics will affect possible algorithms that attempt to make use of these networks, but little is known of these impacts.

Accordingly, we developed a CommunityNetSimulator (CNS), a simulator that combines various network models, as well as various new social network analysis techniques that are useful to study online community (or virtual organization) network formation and dynamics.

The paper is organized as follows: First, in the next section, we discuss social networks in online communities and their implications, as well as review related work. Second, we describe our CommunityNetSimulator (CNS) and its functionality. Third, using the example of a real-world question and answer forum, we show why simulation is a powerful method to study online community networks. Finally, we discuss CNS' limitations and our future work.