Computational methods for social network analysis

Search algorithms that have been developed in the discipline computer science can be applied to the analysis of social networks. These algorithms generally provide two capabilities useful for network analysis: very efficient means of “visiting” every node in a network, and a method of generating all possible paths through a network. The basic search algorithm is called the depth first search algorithm. To implement this algorithm efficiently requires the use of data structures not commonly used in social network analysis at the present time, the singly linked list, and the doubly linked list (sparse matrix). This paper describes how to use depth first search based algorithms and linked list data structures for analyzing the connectivity of social networks. We also propose new measures of connectivity, and use these measures to identify structural properties of networks that capture the connectivity of the network.