Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three
years. Compared with other deep learning modalities, high-performance hardware …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research
and industry attention due to its high robustness and low computation and memory …

Ahead-of-time data layout optimization by vertex reordering is a widely used technique to
improve memory access locality in graph analysis. While reordered graphs yield better …

Graph processing applications are notorious for exhibiting poor cache locality due to an
irregular memory access pattern. However, prior work on graph reordering has observed …

Due to the limited capacity of GPU memory, the majority of prior work on graph applications
on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware …

Detecting strongly connected components (SCCs) in a directed graph is crucial for
understanding the structure of graphs. Most real-world graphs have one large SCC that …

Abstract The Normal Mode Analysis (NMA) is a standard approach to elucidate the
anisotropic vibrations of macromolecules at their folded states, where low-frequency …

Graph-related applications have experienced significant growth in academia and industry,
driven by the powerful representation capabilities of graph. However, efficiently executing …

Ordering vertices of a graph is key to minimize fill-in and data structure size in sparse direct
solvers, maximize locality in iterative solvers, and improve performance in graph algorithms …