Sedigheh abbasghorbani - Young Researchers and Elite Club, Chalus Branch, Islamic Azad University, Chalus, Iran
arash sharifi - Department of Computer Science and Research Branch,Islamic Azad University,Tehran,Iran

Identify key nodes and predict the behavior of users on social networks has attracted many enthusiasts in academia and industry. There are different methods used to calculate the degree of importance of the key nodes in social networks that examined the communication of each node in the network with all other nodes and referred one degree to each of this node. Based on these degrees, trends in user behavior will be extracted. In fact, we use from social networks data to check customer behavior And with introduction of an alternative algorithm based on sequence patterns mining algorithms on sequences extracted by Depth-First Search (DFS) of the social network graph, the size of the graph for a faster extraction trends are reduced. Innovative approach to manage large social networks and redundant nodes by combining sequence patterns structure on the body of social network traversal, and thus significant reduction in computational overhead of trend mining and increase in speed of calculate them especially in dynamic networks that we encountered with the production of large amounts of data. The results of this new proposed method show in case study on large and dynamic real dataset of users contact information in Twitter from their mobile phone messages exchanged during six consecutive months. traversal and reduce the size of social network, cause a significant gap between the values of declined network And the main network that this subject Improved in the proposed model in this paper and compared with a unique algorithm called SMI. This paper shows that The proposed algorithm with track down the nodes with less betweenness centrality being able to remove a large number of nodes and greatly reduce the computational overhead, along with, reduction the betweenness centrality gap In comparison of other approach.

Trend mining, Sequence patterns, Social networks, redundant node, Betweenness centrality