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Deep Dynamic Relational Classifiers : Exploiting Dynamic Neighborhoods in Complex Networks

Using information about a node’s link, neighborhood, in classification models of node behavior typically improves predictive performance in complex network domains. For example, in social networks, friends can provide additional information about a user’s preferences, attitudes, and behaviors. Recent work in relational learning and social network mining has focused on developing models to exploit this type of information. Despite the fact that link information is often changing over time (e.g., emails, colocation events, transactions), there are few methods that model these dynamics with the aim of improving node classification. The majority of current methods either summarizes the temporal information into link weights or aggregates over time to produce a static network. In this work, we consider whether modeling the dynamics in a node’s neighborhood can improve predictive performance. Our key insight is to use a convolutional neural network with max pooling to aggregate over the dynamics. Our model, the Deep Dynamic Relational Classifier (DDRC), represents a node’s neighbors over time as a sequence, but the use of max pooling focuses the model on the existence of key events rather than the full temporal ordering. This is in contrast to previous approaches that focus on the full set of events over time. Our experimental results show that DDRC outperforms several competing baselines, including previous relational and temporal classifiers.