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Link prediction in social networks aims at estimating the likelihood of the appearance of a new link between two nodes, based on the existing links and the attributes of the nodes. Many methods for link prediction problem in social networks have been proposed in literature. Here some of the existing leading methods are deeply studied and analyzed. Accordingly I come out a few new methods for link prediction in social networks. All the proposed methods work under the integrated analysis of topological structure and real-world features for a particular network. As a co-authorship network is a true social network, these networks are considered for verifying the effectiveness of the existing as well as proposed link prediction methods. All the existing as well as proposed methods are found to perform much better over the random predictor. Again it is found that most of the proposed methods give better results than the existing leading methods considered not only on first data domain(1994-99)where 1994-to-96 as training period and 1997-99 as testing period but on second data domain(2007-12)also where 2007-09 as training period and 2010-12 as testing period.