The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network.

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Abstract: Because of the increasing dependence on electronic means for communication, people’s experiences become more virtual and less tangible. But these digital experiences are usually recorded, and they could be traced and analyzed. The persistent data generated by online interactions could be analyzed with different methodologies, like content analysis or thread analysis. However, these methods could be time-consuming or overlook structural characteristics of the interactions. Social network analysis (SNA) techniques have been applied to a variety of problems and they have been successful in uncovering relationships not seen with any other traditional method. Also, visualization techniques are important aids in helping researchers understand social and conversational patterns in online interactions. SNA techniques paired with recent developments in software for visualization could help provide a clearer picture of what is happening in the online environment. In this work, SNA techniques and visualizations were used to examine interactions in an online asynchronous forum. The methodologies to create the visual objects that represent these online interactions are shown and the utility of these images as devices for data pattern recognition is discussed. Key words: social network analysis (SNA) online interact 1.

We analyse 10,920 shortest path connections between 105 members of an interviewing bureau, together with the equivalent conceptual, or ‘small world ’ routes, which use individuals ’ selections of intermediaries. This permits the first study of the impact of accuracy within small world chains. The mean small world path length (3.23) is 40 % longer than the mean of the actual shortest paths (2.30), showing that mistakes are prevalent. A Markov model with a probability of simply guessing an intermediary of 0.52 gives an excellent fit to the observations, suggesting that people make the wrong small world choice more than half the time. © 2005 Elsevier B.V. All rights reserved.

Abstract- Recently, there has been a sharp increase on the scholars ’ collaborations and there are pros and cons by the effect of scientific collaboration on each scholar’ performance. Most of previous researches study the microlevel collaboration network to investigate the effects of scholars ’ collaboration network structure on their performance but to our knowledge there so few macro-level collaboration network studies to evaluate the association between academic communities network structure and the communities ’ academic performance. In this study, we analyze scientific collaboration network structure and network attributes of five information schools and test if there is any link between them and their academic performance. Analysis of collected data shows that the communities ’ which are lower density and lower network degree centrality (more decentralized) have higher performance. This could be as a result of share more redundant knowledge in the dense and centralized scientific collaboration networks, which is an obstacle for innovation and new ideas.

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