Graph embeddings have been tremendously successful at producing node representations that are discriminative for downstream tasks. In this paper, we study the problem of graph transfer learning: given two graphs and labels in the nodes of the first graph, we wish to predict the labels on the second graph. We propose a tractable, non-combinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random.

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two critical challenges to addressing this paradigm: (a) transferring knowledge from old to new classes, and (b) incorporating knowledge learned from new classes back to the original model. We propose Class Discovery Kernel Network with Expansion (CD-KNet-Exp), a deep learning framework, which utilizes the Hilbert Schmidt Independence Criterion to bridge supervised and unsupervised information together in a systematic way, such that the learned knowledge from old classes is distilled appropriately for discovering new classes. Compared to competing methods, CD-KNet-Exp shows superior performance on three publicly available benchmark datasets and a challenging real-world radio frequency fingerprinting dataset.

This paper presents an improvement of the ELMVIS+ method that is proposed for fast nonlinear dimensionality reduction. The ELMVIS++C has an additional supervised learning component compared to ELMVIS+, which is originally an unsupervised method as like the majority of the other dimensionality reduction method. This component prevents samples under the same class being separated apart from each other. In this improved method, the importance of the supervised component can be further tuned to have different level of influence. The test results on four datasets indicate that the proposed improvement not only maintains the performance of ELMVIS+, but also is extremely beneficial for certain applications where the visualization of the data in relation with the class becomes an important issue.