R. Rabbany, J. Chen and O. R. Zaiane, “Top Leaders Community Detection Approach in Information Networks,” Proceedings of the 4th SNA-KDD Workshop on Social Network Mining and Analysis, The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, 2010.

has been cited by the following article:

• TITLE: Determining Leaders and Communities on Networks Using Neighborhood Similarity

  AUTHORS: Benjamin Valyou, Brian Dickinson, Wei Hu

  KEYWORDS: Community Detection; Community Leaders; Node Importance; Centrality; Neighborhood Similarity; Modularity

  JOURNAL NAME: Social Networking, Vol.3 No.1, January 23, 2014

  ABSTRACT: Networks are used to represent interactions in a wide variety of fields, like biology, sociology, chemistry, and more. They have a great deal of salient information contained in their structures, which have a variety of applications. One of the important topics of network analysis is finding influential nodes. These nodes are of two kinds —leader nodes and bridge nodes. In this study, we propose an algorithm to find strong leaders in a network based on a revision of neighborhood similarity. This leadership detection is combined with a neighborhood intersection clustering algorithm to produce high quality communities for various networks. We also delve into the structure of a new network, the Houghton College Twitter network, and examine the discovered leaders and their respective followers in more depth than which is frequently attempted for a network of its size. The results of the observations on this and other networks demonstrate that the community partitions found by this algorithm are very similar to those of ground truth communities.