Ismail Günes

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We propose a link prediction method for evolving networks. Our method first computes a number of different node similarity scores (e.g. Common Neighbor, Preferential Attachment, Adamic–Adar, Jaccard) and their weighted versions, for different past time periods. In order to predict the future node similarity scores, a powerful time series forecasting model,(More)
Evolving heterogeneous networks, which contain different types of nodes and links that change over time, appear in many domains including protein–protein interactions, scientific collaborations, telecommunications. In this paper, we aim to discover temporal information from a heterogenous evolving network in order to improve node classification. We propose(More)
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