***** To join INSNA, visit http://www.insna.org ***** I have the following problem: 100 people on Twitter. And 3 Networks between them: Friend & Follower (FF) connections, AT-Interactions (edge A-->B ~ A writes @B ), RT-Interactions (edge A-->B A retweets one of B's tweets). I want to find out how much the centrality of a person in the FF and AT network influences the centrality in the RT network (aka. people with high centrality in the RT network are people that have been retweeted often) Now I have 2 problems: Problem 1: It turns out that there are people that have FF connections but don't get mentioned at all and don't get retweeted, therefore their in-degrees and centrality metrics in the AT and RT networks are 0. Should I exclude these people from the analysis? The argument for doing this is similar to the assumptions that have to hold for e.g. SIENA when modeling network evolution. Here actors also have to be existing in all panels. Problem 2: Is it feasible to to perform such a regression given the nature of the log-normal or powerlaw distribution of in-degrees or centrality scores? Bonus question: If I were to model these correlation of these networks in pNet and try to capture some sort of centrality effect, is that possible? Would it simply be incoming-network ties effect or someting like that? Best Regards Thomas Plotkowiak Research Assistant MCM Institute _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.