79 citations · 218 across the 21 of their papers we have counts for
12 papers · 1 filter
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
Ousmane Touat, Julian Stier, Pierre-Edouard Portier +1
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating gra…
The Importance of Future Information in Credit Card Fraud Detection
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer +1
Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively…
Experiments on Properties of Hidden Structures of Sparse Neural Networks
Julian Stier, Harshil Darji, Michael Granitzer
Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning…
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors
M. Ben Amor, J. Stier, M. Granitzer
Deep learning models have been shown to be vulnerable to adversarial attacks. This perception led to analyzing deep learning models not only from the perspective of their performan…
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level
Mathieu Garchery, Michael Granitzer
Previous works on the CERT insider threat detection case have neglected graph and text features despite their relevance to describe user behavior. Additionally, existing systems he…
Investigating Extensions to Random Walk Based Graph Embedding
Joerg Schloetterer, Martin Wehking, Fatemeh Salehi Rizi +1
Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches. However, most of th…