Generative Graph Convolutional Network for Growing Graphs
This work addresses the cold start problem in social networks and recommendation systems by enabling generative modeling for new nodes without relying on topological features, though it appears incremental as it builds on existing graph representation and generation methods.
The paper tackles the problem of generating representations for isolated new nodes in growing graphs, which is crucial for applications like social networks and recommendation systems, by proposing a unified generative graph convolutional network that learns adaptive node representations through graph generation sequences and achieves superior performance on benchmark citation network datasets.
Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nodes without nontrivial modifications. The challenge arises due to the fact that learning to generate representations for nodes in observed graph relies heavily on topological features, whereas for new nodes only node attributes are available. Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data. We optimize over a variational lower bound that consists of a graph reconstruction term and an adaptive Kullback-Leibler divergence regularization term. We demonstrate the superior performance of our approach on several benchmark citation network datasets.