Visualization of Communication Patterns in Collaborative Innovation Networks - Analysis of Some W3C Working Groups

Abstract

Collaborative Innovation Networks (COINs) are groups of self-motivated individuals from various parts of an organization or from multiple organizations, empowered by the Internet, who work together on a new idea, driven by a common vision. In this paper we report first results of a project that examines innovation networks by analyzing the e-mail archives of some W3C (WWW consortium) working groups. These groups exhibit ideal characteristics for our purpose, as they form truly global networks working together over the Internet to develop next generation technologies. We first describe the software tools we developed to visualize the temporal communication flow, which represent communication patterns as directed acyclic graphs. We then show initial results, which revealed significant variations between the communication patterns and network structures of the different groups. We were also able to identify distinctive communication patterns among group leaders, both those who were officially appointed and other who were assuming unofficial coordinating roles.

A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network

“…A centrality measure can be used to identify the most important people at the center of a network or those that are well connected. Various type of centrality measures such as degree [13,14] ,closeness [15,16,17], betweenness [18,19,20], information [21,22], eigenvector [23.24], and dependence centrality [25,26] have been used for characterizing the social behaviour and connectedness of nodes within networks. The centrality measures find more active people from one or more subgroups.…”

Flink: Semantic Web technology for the extraction and analysis of social networks

“…Note that we do not factor in the page count of the interests, since we are only interested in the expertise of the individual relative to himself. 7 The resulting measure is again a zero or positive real term with a power-law distribution. We assign the expertise to an individual if the logarithm of this value is at least one standard deviation higher than the mean of the logarithmic values.…”

Social Networks and the Semantic Web

“…For example, in the HP case the content of messages had to be ignored by the researchers and the data set could not be shared with the community. In the work of Peter Gloor and colleagues, the source of these data for analysis is the archive of the mailing lists of a standard setting organization, the World Wide Web Consortium (W3C) [Gloor et al, 2003]. The W3C -which is also the organization responsible for the standardization of Semantic Web technologies-is unique among standardization bodies in its commitment to transparency toward the general public of the Internet and part of this commitment is the openness of the discussions within the working groups.…”

“…For example, email communication in research and standardization settings are the source of social networks in [Gloor et al, 2003] and [Adamic and Adar, 2005], while other studies extract social networks from the content of web pages [Kautz et al, 1997, Mori et al, 2004 or -somewhat less successfullyby analyzing the linking structure of the Web [Heimeriks et al, 2003]. As the first to publish such a study, Paolillo and Wright offer a rough characterization of the FOAF 4 web in .…”