Reza Bakhshandeh’s research while affiliated with Shiraz University and other places

Social networks play an increasingly important role in today's society. Special characteristics of these networks make them challenging domains for the search community. In particular, social networks of users can be viewed as search graphs of nodes, where the cost of obtaining information about a node can be very high. This paper addresses the search problem of identifying the degree of separation between two users. New search techniques are introduced to provide optimal or near-optimal solutions. The experiments are performed using Twitter, and they show an improvement of several orders of magnitude over greedy approaches. Our optimal algorithm finds an average degree of separation of 3.43 between two random Twitter users, requiring an average of only 67 requests for information over the Internet to Twitter. A near-optimal solution of length 3.88 can be found by making an average of 13.3 requests.

In todays life, everybody who knows the Internet is familiar with web-based search engines and micro-blogging social networks. People from all over the world are using search engines as well as sending a huge amount of text messages to micro-blogging social networks in the daily life. This work addresses the problem of using micro-blogging social networks to personalize search engine results. We introduce a framework which combines the similarity of results fetched from a non-personalized search engine and the users's interests extracted from the micro-blogging social networks. We used the Twitter social network as the experimental domain to test the performance of proposed algorithms and evaluate the results by applying the algorithms on some of the well-known users who we know their interests.

According to the small-world concept, the entire world is connected through short chains of acquaintances. In popular imagination this is captured in the phrase six degrees of separation, implying that any two individuals are, at most, six handshakes away. Social network analysis is the understanding of concepts and information on relationships among interacting units in an ecological system. In this analysis the properties of the actors are explained in terms of the structures of links amongst them. In general, the relational links between the actors are primary and the properties of the actors are secondary. This paper presents two methods to calculate the average degree of separation between the actors or nodes in a graph. We apply this approach to other random graphs depicting social networks and then compare the characteristics of these graphs with the average degree of separation.