Link prediction via linear optimization

Link Prediction via Matrix Factorization

We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields bett...

Link Prediction via Matrix Completion

Ratha Pech, Hao Dong1,2,∗, Liming Pan, Hong Cheng, Zhou Tao1,2,∗ 1 CompleX Lab, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China 2 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China and 3 Center for Robotics, University of Electronic Science and Technology of China, Ch...

Robustness optimization via link additions

Link Prediction via Generalized Coupled Tensor Factorisation

This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach based on probabilistic interpretation of t...

Accelerating Optimization via Adaptive Prediction

We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problemdependent randomization. We first present a series of new regret guarantees that hold at any time and under very minimal assumptions, and then show how different relaxations recover exis...