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Found 6 papers, 1 papers with code
Graph Feature Preprocessor: Real-time Extraction of Subgraph-based Features from Transaction Graphs
In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering and fraud patterns in financial transaction graphs in real time.
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets.
Provably Powerful Graph Neural Networks for Directed Multigraphs
This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks.
Low-Complexity Data-Parallel Earth Mover's Distance Approximations
The Earth Mover's Distance (EMD) is a state-of-the art metric for comparing discrete probability distributions, but its high distinguishability comes at a high cost in computational complexity.
Large-Scale Stochastic Learning using GPUs
In this work we propose an accelerated stochastic learning system for very large-scale applications.
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark
We begin by analyzing the characteristics of a state-of-the-art distributed machine learning algorithm implemented in Spark and compare it to an equivalent reference implementation using the high performance computing framework MPI.
