A Relation Context Oriented Approach to Identify Strong Ties in Social Networks

A Relation Context Oriented Approach to Identify Strong Ties in Social Networks

• October 1st, 2009
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Social network graphs have been found to be an extremely effective tool in the identification of potential perpetrators of criminal activity. These graphs can grow extremely large, as illustrated by an example within this paper that contained over 4.9 million nodes and over 211 million edges. Obviously some reduction of these graphs is essential to their being useful. Further, considerable “noise” (false positive relationships) are generated when the graphs are totally comprehensive. This research transformed the original social network into a relational context-oriented edge-dual graph. This was done by evaluating the quality of the connectivity for each edge to obtain a metric to this effect for each edge. By retaining only the strongest edges the overall graph becomes more reliable and more useful in practice.