Abstract
Abstract: Online Social Network has gained immense traction of users in past decade. Link prediction across social networks has become a new exploration area for researchers, where existing links are investigated and new links are anticipated among billions of online customers. Majority of work in this area focusses on exploring the current status of a particular network at a specific time, without exploring the behavior of the network links as time goes by. Only a Small amount of work has been performed with the consideration of temporal aspect of network. As the interests and interactions of user change over time, the links among nodes become weaker or noisy which affects the prediction accuracy. This paper intend to explore a new integrated temporal method TD score which includes time stamp of interaction and domain similarity information for each pair of unconnected nodes to predict links. Experiment over co-authorship network reveals that link prediction covering time aware domain similarity is effective and efficient approach than traditional ones. Keywords: Co-authorship network; link prediction; node similarity; global feature; temporal feature; domain similarity.
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