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Deep learning of representations_ looking forward
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Networks are everywhere. Popular examples include social networks, the hyperlinked World Wide Web, transportation networks, electricity power networks and biological gene networks. Networks are typically represented as a graph whose vertices represent entities and edges represent links or relationships between these entities. As the pervasiveness and scope of network data increase, there has been significant interest in developing statistical models to learn from networks for prediction or reasoning tasks
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