Overview
The rapid evolution of social network platforms such as Twitter, Facebook, and Instagram has resulted in heterogeneous networks with complex social interactions. Despite providing a rich source of information, the dimensionality of features in these networks poses several challenges to machine learning tasks such as personalization, prediction, and recommendation. Therefore, it is important to ask the question “how to capture such complex interactions between users in simplified dimensions?”. To answer this question, this entry explores network representation learning (NRL), where the objective is to model the complex high-dimensional interactions between nodes in a reduced feature space while simultaneously capturing the neighborhood similarity and community membership.
Information in the modern world flows in the form of graphs such as social networks, biological networks, and World Wide Web. These graphical structures reveal many intriguing characteristics about the...
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Rakesh, V., Tang, L., Liu, H. (2019). Feature Learning from Social Graphs. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_273
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DOI: https://doi.org/10.1007/978-3-319-77525-8_273
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