The rise of digital payments has caused consequential changes in the financial crime
landscape. As a result, traditional fraud detection approaches such as rule-based systems …

Generalized tensor algebra is a prime candidate for acceleration via customized ASICs.
Modern tensors feature a wide range of data sparsity, with the density of non-zero elements …

Given the growing importance of large-scale graph analytics, there is a need to improve the
performance of graph analysis frameworks without compromising on productivity. GraphMat …

The GraphBLAS standard (GraphBlas. org) is being developed to bring the potential of
matrix-based graph algorithms to the broadest possible audience. Mathematically, the …

Graph processing is becoming increasingly prevalent across many application domains. In
spite of this prevalence, there is little research about how graphs are actually used in …

Tor is the second generation onion router supporting the anonymous transport of TCP
streams over the Internet. Its low latency makes it very suitable for common tasks, such as …

We reduce the cost of communication and synchronization in graph processing by analyzing
the fastest way to process graphs: pushing the updates to a shared state or pulling the …

Graph processing is becoming increasingly prevalent across many application domains. In
spite of this prevalence, there is little research about how graphs are actually used in …

Graph algorithms are becoming increasingly important for analyzing large datasets in many
fields. Real-world graph data follows a pattern of sparsity, that is not uniform but highly …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …