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Found 10 papers, 2 papers with code
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems.
Natural language to SQL in low-code platforms
Alongside this, we describe the entire pipeline, which comprises a feedback loop that allows us to quickly collect production data and use it to retrain our SQL generation model.
The GANfather: Controllable generation of malicious activity to improve defence systems
In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system.
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets).
T5QL: Taming language models for SQL generation
Automatic SQL generation has been an active research area, aiming at streamlining the access to databases by writing natural language with the given intent instead of writing SQL.
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1. 7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption.
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Money laundering is a global phenomenon with wide-reaching social and economic consequences.
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset.
ARMS: Automated rules management system for fraud detection
We propose ARMS, an automated rules management system that evaluates the contribution of individual rules and optimizes the set of active rules using heuristic search and a user-defined loss-function.
GoT-WAVE: Temporal network alignment using graphlet-orbit transitions
Dynamic GDVs (DGDVs) were used as a dynamic NC measure within the first-ever algorithms for GPNA of temporal networks: DynaMAGNA++ and DynaWAVE.
