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In this paper, we present a novel transfer learning framework for network node classification. Our objective is to accurately predict the labels of nodes in a target network by leveraging information from an auxiliary source network. Such a transfer learning framework is potentially useful for broader areas of network classification, where emerging new networks might not have sufficient labeled information...
the present study utilizes social computing techniques to enhance the content-based recommender systems. Coined as Enhanced Content-based Algorithm using Social Networking (ECSN), this recommender algorithm is applied in academic social networks to suggest the most relevant items to members of these online societies. In addition to considering user's own preferences, ECSN takes advantage of the interest...
Cultural modeling is an emergent and promising research area in social computing. It aims to develop behavioral models of groups and analyze the impact of culture factors on group behavior using computational methods. Classification methods play a critical role in cultural modeling domain. As various cultural-related datasets possess different properties, for group behavior prediction, it is important...
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