Graph neural networks (GNNs) are gaining popularity as a promising approach to machine
learning on graphs. Unlike traditional graph workloads where each vertex/edge is …

General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental
linear operations in a wide variety of scientific applications. To implement efficient SpGEMM …

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for
large-scale graph analytics, including byte-addressability, low idle power, and improved …

Sparse generalized matrix-matrix multiplication (SpGEMM) is a fundamental operation for
real-world network analysis. With the increasing size of real-world networks, the single …

In many applications of network analysis, it is important to distinguish between observed and
unobserved factors affecting network structure. To this end, we develop spectral estimators …

We present new sequential and parallel algorithms for wavelet tree construction based on a
new bottom-up technique. This technique makes use of the structure of the wavelet trees …

Smart cities and other cyber-physical systems (CPSs) rely on various scientific, engineering,
business, and social applications that provide timely intelligence for their design, operations …

We propose a novel approach to iterated sparse matrix dense matrix multiplication, a
fundamental computational kernel in scientific computing and graph neural network training …

Graph convolutional networks (GCNs) are becoming increasingly popular as they can
process a wide variety of data formats that prior deep neural networks cannot easily support …

General sparse matrix-sparse matrix (SpGEMM) multiplication is one of the basic kernels in
a great many applications. Several works focus on various optimizations for SpGEMM. To …