Robust Graph Embedding with Noisy Link Weights

We propose β-graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment β-score reduces the influence of contamination and robustly measures the difference between the underlying correct expected weights of links and the specified generative model. The proposed method is computationally tractable; we employ a minibatch-based efficient stochastic algorithm and prove that this algorithm locally minimizes the empirical moment β-score. We conduct numerical experiments on synthetic and real-world datasets.