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Using Pajek to dissect my 2-mode network data on Japanese advertising creatives and the teams that produce winning ads, I make a  now-conventional move—projecting two 1-mode networks. From each of the 1-mode networks, I extract the giant component. Having extracted the giant component I have Pajek compute the normalized degree, closeness, betweenness and eigenvalue centrality (hubs and authorities) vectors. I then use the Vectors&gt;Info command to examine the Spearman rank correlations between the four vectors. I discover a curious phenomenon. In the case of the 1-mode network of individual creators, the highest correlation (.7~.8) is between normalized degree and betweenness. In the case of the 1-mode network of teams, however, the highest correlation (again in the .7~.8 range) is between normalized degree and closeness. <div>
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<br></div><div>Has anyone else encountered this phenomenon? Is this a common and/or mathematically predictable result? If so, please tell me how it works or where to look for an explanation.</div><div><br></div><div>John<br clear="all">
<div><br></div>-- <br>John McCreery<br>The Word Works, Ltd., Yokohama, JAPAN<br>Tel. <a href="tel:%2B81-45-314-9324" value="+81453149324" target="_blank">+81-45-314-9324</a><br><a href="mailto:[log in to unmask]" target="_blank"><a href="/cgi-bin/wa?LOGON=A3%3Dind1112%26L%3DSOCNET%26E%3Dquoted-printable%26P%3D763286%26B%3D--000e0cd5b81cb0cc1d04b5363c5c%26T%3Dtext%252Fhtml%3B%2520charset%3Dwindows-1252" target="_parent" >[log in to unmask]</a></a><br>
<a href="http://www.wordworks.jp/" target="_blank">http://www.wordworks.jp/</a><br>

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