Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

Deep neural networks and in particular, deep neural classifiers have become an integral
part of many modern applications. Despite their practical success, we still have limited …

Graph alignment aims to identify node correspondence across multiple graphs, with
significant implications in various domains. As supervision information is often not available …

Network alignment plays an important role in a variety of applications. Many traditional
methods explicitly or implicitly assume the alignment consistency which might suffer from …

Recent years have witnessed rapid developments on social recommendation techniques for
improving the performance of recommender systems due to the growing influence of social …

The goal of molecule optimization is to optimize molecular properties by modifying molecule
structures. Conditional generative models provide a promising way to transfer the input …

Online learning has attracted a large number of participants and is increasingly becoming
very popular. However, the completion rates for online learning are notoriously low. Further …

Network alignment, the process of finding correspondences between nodes in different
graphs, has many scientific and industrial applications. Existing unsupervised network …

Graph alignment, which aims at identifying corresponding entities across multiple networks,
has been widely applied in various domains. As the graphs to be aligned are usually …