Representation Learning on Dynamic Network of Networks

Network of networks (NoN) where each node of the main network represents a domain-specific network, is a powerful multi-network model to capture the relationships among entities at both coarse and fine granularities. Existing graph convolutional networks (GCN) learn node representations either on a single network or multiple networks while overlooking the relationships among different networks (e.g., main network structure). In addition, many real-world networks often evolve over time, which makes it imperative yet even more challenging to leverage temporal information for node representation learning. In this paper, we study the node representation learning problem on dynamic network of networks. The key idea of designing the static model is the predict-then-propagate strategy such that node representations are obtained by propagating the initial representations of common nodes which are shared across domain-specific networks. To leverage the temporal information underlying dynamic NoN, we extend the static model by a gated recurrent unit (GRU) to capture the dynamics behind cross-network consistency and a self-attention mechanism to learn the dependence of nodes on their historical representations. With these components, we propose an end-to-end model DraNoN to learn node representations on dynamic NoN. We conduct experiments on the dynamic network alignment task, which demonstrate the superior performance of DraNoN compared with the state-of-the-arts.