***** To join INSNA, visit http://www.insna.org ***** Excellent point Christian and I agree that Tom is right. In the Marketing Science commentary I pointed to I make this exact point - experiments are difficult and observational data is abundant. That's why we need statistical methods that try to tease out contagion from selection/homophily and other confounds. In that review I list your work with Tom Snijders as one of those methods (as well as our own dynamic propensity score methods published in the PNAS -- I pointed to this article in a previous post as well). Both of these are excellent dynamic methods for teasing out influence/contagion from other confounds. But, again, the problem is *very* difficult in my opinion. The reason I think we have to focus on this problem as a community and make it such a high priority in networks research (and thus the reason I focus on it so much in my own research) is because a) it is critical to knowing when we are observing "network effects" and when we are not, which is then in turn critical to policy choices...(for example peer-to-peer contagion management policies won't work if our estimates tell us contagion is at play when it really isn't) and b) because it is such a hard nut to crack. For those of you that will be at the SONIC/ANN/NICO conference at Northwestern this weekend, Michael Macy and I will have a session on "Causality in Networks" on Saturday. Here is the schedule of the Workshop portion of the event: http://sonic.northwestern.edu/events/webnetsciworkshop/conference-schedule/ And, here is the abstract of my talk for those that are interested: Title: "Causality in Networks" Abstract: Many of us are interested in whether "networks matter." Whether in the spread of disease, the diffusion of information, the propagation of behavioral contagions, the effectiveness of viral marketing, or the magnitude of peer effects in a variety of settings, a key question that must be answered before we can understand whether networks matter, is whether the statistical relationships we see can be interpreted causally. Several sources of bias in analysis of interactions and outcomes among peers can confound assessments of peer influence and social contagion in networks. If uncorrected, these biases can lead researchers to attribute observed correlations to causal peer influence, resulting in misinterpretations of social network effects as well as biased estimates of the potential effectiveness of different intervention strategies. Several approaches for identifying peer effects have been proposed. However, randomized trials are considered to be one of the most effective ways to obtain unbiased estimates of causal peer effects. I will review a) the importance of establishing causality in networks, b) the various methods that have been proposed to address causal inference in networks, and in particular focus on c) the use of randomized trials to establish causality. I will provide an example from a randomized field experiment we conducted on a popular social networking website to test the effectiveness of "viral product design" strategies in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. In addition to estimating the effects of viral product design on social contagion and product diffusion, our work also provides a model for how randomized trials can be used to identify peer influence effects in networks. Best Sinan Sinan Aral Assistant Professor, NYU Stern School of Business. Research Affiliate, MIT Sloan School of Management. Personal Webpage: http://pages.stern.nyu.edu/~saral SSRN Page: http://ssrn.com/author=110270 WIN Workshop: http://www.winworkshop.net Twitter: http://twitter.com/sinanaral On 2/27/2011 12:38 PM, Christian Steglich wrote: > ***** To join INSNA, visit http://www.insna.org ***** Hi Steve, > > as I understand, your network is static and not changing in itself? If > it does change as well, this can potentially undermine all sorts of > conclusions you may want to draw from an analysis, as any behaviour > association between connected actors could be due to selection effects > as well. > > See here and article I participated in for some methodological > arguments, a brief critical review of methods, and a proposal to use > actor-based modelling in Snijders' tradition: > http://dx.doi.org/10.1111/j.1467-9531.2010.01225.x > > For a static network, the most serious confounders of contagion are > context effects - so you should capture all context information you > can. Cohen-Cole & Fletcher wrote a funny piece illustrating how some > analysis methods, when failing to include context, can provide > patently misleading results (they showed how some methods would > diagnose e.g. body height as socially contagious): > http://dx.doi.org/10.1136/bmj.a2533 > > In general, I agree with Sinan that experiments are the best way to > obtain "unequivocal" contagion effects, but Tom is right when pointing > out that this is very often is not possible in applied research > settings... > > Greetings, > Christian > > > Am 25/02/2011 20:28, schrieb Steve Eichert: >> ***** To join INSNA, visit http://www.insna.org ***** Hello SOCNET, >> >> I'm looking for books, papers, algorithms, and/or ideas on how best >> to measure contagion in a network. We have longitudinal behavior >> data for all actors in a directed network and want to calculate the >> degree of contagion occurring between all connected nodes. We would >> like to use the calculated "contagion score" to identify nodes that >> we can do further analysis on, as well as to measure the overall >> level of contagion in the network. The longitudinal behavior data we >> have indicates how much of something the nodes within the network are >> using over time. We're interested in better understanding the >> algorithms folks are using for "adoption contagion" (someone who has >> already adopted influences a non adopter to adopt) as well as >> "behavior contagion" (a high user influences those connected to them >> to use more). >> >> Thoughts? >> >> Thanks, >> Steve >> _____________________________________________________________________ >> 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. > > > -- > _ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _ > > Christian Steglich, researcher > Faculty of Behavioural and Social Sciences > University of Groningen > Grote Rozenstraat 31 > 9712 TG Groningen > The Netherlands > > fon +31-(0)50-363 6189 > fax +31-(0)50-363 6226 > > http://www.gmw.rug.nl/~steglich/ > _ __ ___ ____ ___ __ _ __ ___ ____ ___ __ _ > > _____________________________________________________________________ > 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. _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). 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