A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs
This work addresses the problem of enhancing prediction accuracy in dynamic graph analysis for applications where graph structure and node features are interdependent, representing an incremental improvement over separate prediction methods.
The paper tackles the joint prediction of links and node features in dynamic graphs by optimizing a joint regularization objective, showing that this approach improves prediction performance over both graph evolution and node features in empirical tests.
We consider the two problems of predicting links in a dynamic graph sequence and predicting functions defined at each node of the graph. In many applications, the solution of one problem is useful for solving the other. Indeed, if these functions reflect node features, then they are related through the graph structure. In this paper, we formulate a hybrid approach that simultaneously learns the structure of the graph and predicts the values of the node-related functions. Our approach is based on the optimization of a joint regularization objective. We empirically test the benefits of the proposed method with both synthetic and real data. The results indicate that joint regularization improves prediction performance over the graph evolution and the node features.