Social Networks are gaining importance due to their enablement of modeling various types of interactions among individuals, communities and organizations. Network Topologies play a major role in analyzing the social networks for a variety of business application scenarios such as finding influencers in product campaigning and virtual communities to recommend music downloads. Social networks are dynamic in nature and detection of topologies from these networks presents a host of new challenges. In this paper, we present approaches for topology discovery, particularly star, ring and mesh, based on the measures of network centrality. These approaches facilitate an efficient way of discovering topologies for analyzing large social networks. We also discuss experiments on DBLP dataset to show the viability of our proposed approach.

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Adoption of Clinical Decision Support Systems in the process of clinical decision process has been gaining attention in recent times. Such intelligent decision support systems need frequent access to historic medical data such as medical images and associated reports. Content Based Image Retrieval has been preferred choice of technique for such smart retrieval of medical images based on their content. In this paper, we introduce a new approach for deriving edge based features for retrieval of medical images. Texture edges are known to represent the tissue boundaries better than intensity edges in medical images. Therefore, we use texture edges for describing image structure instead of traditional approach of using intensity edges. We demonstrate that retrieval performance at organ level in medical images using texture edges is superior to intensity edges.