In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

Code retrieval is to find the code snippet from a large corpus of source code repositories that
highly matches the query of natural language description. Recent work mainly uses natural …

While the celebrated graph neural networks (GNNs) yield effective representations for
individual nodes of a graph, there has been relatively less success in extending to the task …

We present a novel mechanism to improve the accuracy of the recently-introduced class of
graph random features (GRFs). Our method induces negative correlations between the …

We introduce in this paper the mechanism of graph random features (GRFs). GRFs can be
used to construct unbiased randomized estimators of several important kernels defined on …

Graph matching plays a central role in such fields as computer vision, pattern recognition,
and bioinformatics. Graph matching problems can be cast as two types of quadratic …

Abstract Background and Objective Predicting patients' survival from gigapixel Whole-Slide
Images (WSIs) has always been a challenging task. To learn effective WSI representations …

Recent years have witnessed the significant success of applying graph neural networks
(GNNs) in learning effective node representations for classification. However, current GNNs …

With continued scaling, the transistor aging induced by hot carrier injection (HCI) and bias
temperature instability (BTI) causes an increasing failure of nanometer-scale integrated …

We investigate novel random graph embeddings that can be computed in expected
polynomial time and that are able to distinguish all non-isomorphic graphs in expectation …