SNA Metrics

SNA Metrics is a general-purpose service for computing social network charasteristics from sociomatrix data. SNA metrics service is capable of interpreting proprietary xml formatted node-edge (actor-connection) file. Node-edge (actor-connection) format represents a social network, its nodes (actors) and its edges (connections) are transformed into sociomatrix format. Social network analysis is performed on given social network and results are represented to the user in proprietary SNA metrics xml-format.

Node-edge input format

Proprietary xml-format for representing a sociograph in node-edge (actor-connection) format.

<?xml version="1.0" encoding="utf-8"?>
<log xmlns:xs="http://www.w3.org/2001/XMLSchema">
   <actorlist>
      <actor id="anni anonyymi">
      </actor>
      <actor id="Oy Ab">
      </actor>
      <actor id="iida insinoori">
      </actor>
   </actorlist>
   <connectionlist>
      <connection>
         <source>anni anonyymi</source>
         <target>Oy Ab</target>
      </connection>
      <connection>
         <source>iida insinoori</source>
         <target>Oy Ab</target>
      </connection>
   </connectionlist>
</log>

SNA Metrics Output format

Prioprietary output format for SNA Metrics. Node degree, closeness centrality and degree prestige are calculated for each given actor.

<?xml version="1.0" ?>
<snametrics>
 <actor id="aaron-lee">
  <metric type="degree">
   1.0
  </metric>
  <metric type="centrality">
   0.00368100817341
  </metric>
  <metric type="prestige">
   0.468944099379
  </metric>
 </actor>
<actor id="adam-lindemann">
  <metric type="degree">
   1.0
  </metric>
  <metric type="centrality">
   0.00284557956873
  </metric>
  <metric type="prestige">
   0.309426229508
  </metric>
 </actor>
</snametrics>



Functions

SNA Metrics service uses several functions from non_directed.py library which can be used to analyze sociomatrices.

  • nodes
    • Returns dimension of square sociomatrix
    • Input: n times n size Sociomatrix
    • Output: Dimension of matrix, n
  • matrix_power
    • Returns power matrix of given matrix and exponential
    • Input: sociomatrix, exponential
    • Output: power matrix
  • geodesics
    • Computes geodesics of given sociomatrix
    • Can be computationally slow for big matrices
    • Input: sociomatrix
    • Output: sociomatrix
  • closeness
    • Computes closeness centrality of given sociomatrix
    • Input: sociomatrix
    • Output: closeness centrality array
  • closeness_var
    • Computes variance of closeness centrality of given sociomatrix
    • Input: sociomatrix
    • Output: closeness centrality variance array
  • deg_centrality
    • Computes Degree centrality of given sociomatrix
    • Input: sociomatrix
    • Output: degree centrality array
  • deg_centrality_var
    • Computes variance of Degree centrality of given sociomatrix
    • degree centrality variance array
    • Input: sociomatrix
    • Output: degree centrality variance array
  • remove_isolates
    • Removes isolated nodes from sociomatrix, returns modified sociomatrix
    • Indices of removed nodes returned in another variable
    • Input: sociomatrix
    • Output: sociomatrix, removed indices array
  • info_centrality
    • Computes information centrality of given sociomatrix
    • Input: sociomatrix
    • Output: information centrality array
  • asteluku
    • Computes node degree of given sociomatrix
    • Input: sociomatrix
    • Output: node degree array
  • friends_friend
    • Evaluates friends friend connections from sociomatrix
    • Input: sociomatrix
    • Output: sociomatrix
  • socio_to_pajek
    • Transforms sociomatrix into a pajek format
    • Input: sociomatrix
    • Output: pajek.txt -file
  • deg_prestige
    • Computes degree prestige
    • Input: sociomatrix
    • Output: degree prestige array
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Topic revision: r13 - 29 Apr 2010 - 12:58:45 - JaakkoSalonen
 

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