Extending the classification of nodes in social networks

Abstract

Because of computational concerns, social network analysis generally uses only directly connected nodes to perform classification tasks. However, recent research indicates that this method of classification may not consider that nodes in the graph could have different influence over other nodes near them in the graph. It is possible that well-selected nodes may have a stronger importance in a social graph. Here, we analyze methods by which these important nodes may be identified and used to improve the classification of nodes within the social graph. We also show the effect of incorporating these important nodes in social network classification.