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README.md

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-# Anomaly Detection in Networks via Score-Based Models
-This is a repository for my Master's Thesis at Skoltech
+# Anomaly Detection in Networks via Score-Based Generative Models
+This is a repository for an ICML 2023 SPIGM Workshop paper and my Master's Thesis at Skoltech.
 
-In this project, we use [GDSS](https://github.com/harryjo97/GDSS) as a generative model.
+Authors: Dmitrii Gavrilev, Evgeny Burnaev (research advisor)
 
 ### Abstract
 
-Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. We leverage score-based models by first constructing ego-centric graphs for each node. Then, we assign anomaly scores based on the dissimilarity between ego-graphs and their reconstructions or density estimation. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy and show that generative models might struggle to accurately reconstruct it.
+Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it.
+
+In this project, we use [GDSS](https://github.com/harryjo97/GDSS) as a generative model.