***** To join INSNA, visit http://www.insna.org ***** Hey All, I am working on my dissertation in social psychology, and in the course of reading Baker and Faulkner (1993), I suspect an error in computation of graph closeness centralization. Their actor closeness (Eq. 3, p. 848) they call "farness" and later refer to it in note 8 as Sabidussi's index. Wasserman and Faust (1994, p. 184) indicate that Sabidussi's index is actually the reciprocal of this value. Further, their equation is not standardized by multiplying by g - 1 (Wasserman & Faust, Eq. 5.8), causing a problem for comparing across networks of different size. One of their hypotheses was that the network with high information processing (turbines conspiracy) should have the lowest graph centralization, which I understand. They give Freeman's equation in their Equation 4, but they compute closeness graph centralization using their farness index rather than the standardized closeness index. I interpret their number as unstandardized graph decentralization, but they interpret it as regular centralization. In Table 1, they give "Sabidussi graph centralization (farness)" numbers, and the turbines network has the highest value. They conclude, "This illegal network should be sparse and decentralized. This expectation is not supported by the data. The turbines conspiracy network exhibits the highest density and is the most centralized" (Baker & Faulkner, p. 850). Am I correct in reinterpreting their number as unstandardized graph decentralization? If so, am I correct in surmising that their conclusion is backwards? If this is the case, is there a way to take their number and the network size and algebraically obtain proper graph centralization? I am rather confused by this and would greatly appreciate any advice you can offer. David M. Ouellette Psychology Department, Social Division Virginia Commonwealth University References Baker, W. E., & Faulkner, R. R. (1993). The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. American Sociological Review, 58, 837-860. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press. _____________________________________________________________________ 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.