Survey on Effective Use of Sampling Technique for Online Social Network

The web technology aims to enhance connectivity, information sharing on the web have led to the emergence of online social networking service. This is noticeable by the host of activity and social interaction which discovered in web sites like Facebook, Linkedin, and Twitter etc. At the same time the desire to connect and interact progress far beyond centralized social networking sites and takes the form of ad hoc social networks formed by instant messaging clients, mobile networks. While interactivity with people beyond one’s contact list is currently not possible, the implied social networking structure is in place. Taking of large assumption of these networks, there has been growing the attentiveness to search the underlying information in order to improve on information retrieval tasks of social peers. These tasks are in the center of many application domains. We show that our algorithms can improve sampling of nodes in a network structure, assumes detail for each user in the network is available. The general methods are described here and can probably be adopted in various types of strategies that efficiently collect the information from a social graph. Our main contribution is to select the samples by making use of sampling-based algorithms (selectivity estimation via k-mean sampling) that given a user in a social network quickly obtain a near uniform random sample of nodes in its neighborhood and generate the graph.