Crawling Social Web with Cluster Coverage Sampling

Social network can be viewed as a huge container of nodes and relationship edges between the nodes. Covering every node of social network in the analysis process faces practical inabilities due to gigantic size of social network. Solution to this is to take a sample by collecting few nodes and relationship status of huge network. This sample can be considered as a representative of complete network, and analysis is carried out on this sample. Resemblance of results derived by analysis with reality majorly depends on the extent up to which a sample resembles with its actual network. Sampling, hence, appears to be one of the major challenges for social network analysis. Most of the social networks are scale-free networks and can be seen having overlapping clusters. This paper develops a robust social Web crawler that uses a sampling algorithm which considers clustered view of social graph. Sample will be a good representative of the network if it has similar clustered view as actual graph.