By using Data Augmentation (DA), we present a new method to enhance Graph
Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised …

General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

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 …

Breadth‐First Search is an important kernel used by many graph‐processing applications. In
many of these emerging applications of BFS, such as analyzing social networks, the input …

Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

Generalized sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many
high performance graph algorithms as well as for some linear solvers, such as algebraic …

High-performance implementations of graph algorithms are challenging to implement on
new parallel hardware such as GPUs because of three challenges:(1) the difficulty of coming …

We present topic-regression multi-modal Latent Dirich-let Allocation (tr-mmLDA), a novel
statistical topic model for the task of image and video annotation. At the heart of our new …

As graphs continue to grow, external memory graph processing systems serve as a
promising alternative to inmemory solutions for low cost and high scalability. Unfortunately …