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Link Prediction in Graphs with Autoregressive Features
Emile Richard, Stephane Gaiffas, Nicolas Vayatis
In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VA…
Estimation of Simultaneously Sparse and Low Rank Matrices
Emile Richard, Pierre-Andre Savalle, Nicolas Vayatis
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social netw…
Graph Prediction in a Low-Rank and Autoregressive Setting
Emile Richard, Pierre-Andre Savalle, Nicolas Vayatis
We study the problem of prediction for evolving graph data. We formulate the problem as the minimization of a convex objective encouraging sparsity and low-rank of the solution, th…
A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs
Emile Richard, Andreas Argyriou, Theodoros Evgeniou +1
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 p…