Link prediction is a crucial task in analysis of online social networks. It can be used for friend recommendations in social networks. It also has applications in other areas like bioinformatics, information retrieval and e-commerce. Currently, variety of techniques is available to deal with link prediction problem, ranging from local feature based approaches to global feature based approaches. Some recent techniques combine the advantages of these two approaches and comes under hybrid category. all these methods differ from each other with respect to prediction performance in terms of accuracy, efficiency, and generalization ability. In this paper, we survey some representative link prediction techniques under each category. We largely consider three types of approaches. First category is of local feature based approaches which do not exploit the whole network structure. Second category is of global feature based approaches which use the overall path structure of a network. And, finally the hybrid approaches which combine the advantages of above two approaches (local and global) in computation of similarity scores between each pair of nodes in a network. We discuss some recent existing approaches corresponding to these broad categories and analyze their strength and weaknesses. We conclude the paper with discussion on recent developments and future research direction.
This study proposes a novel method for identifying the primary conspirators involved in terrorist activities. To map the information related to terrorist activities, we gathered information from different sources of real cases involving terrorist attacks. We extracted useful information from available sources and then mapped them in the form of terrorist networks, and this mapping provided us with insights in these networks. Furthermore, we came up with a novel centrality measure for identifying the primary conspirators of a terrorist attack. Because the leaders of terrorist attacks usually direct conspirators to conduct terrorist activities, we designed a novel algorithm that can identify such leaders. This algorithm can identify terrorist attack leaders even if they have less connectivity in networks. We tested the effectiveness of the proposed algorithms on four real‐world datasets and conducted an experimental evaluation, and the proposed algorithms could correctly identify the primary conspirators and leaders of the attacks in the four cases. To summarize, this work may provide information support for security agencies and can be helpful during the trials of the cases related to terrorist attacks.
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