Jacob Danovitch

Jacob Danovitch

Applied science @ Microsoft

Publications

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.

Huang, Shenyang, et al. Temporal graph benchmark for machine learning on temporal graphs. Advances in Neural Information Processing Systems 36 (2024). https://proceedings.neurips.cc/paper_files/paper/2023/hash/066b98e63313162f6562b35962671288-Abstract-Datasets_and_Benchmarks.html

Fast and Attributed Change Detection on Dynamic Graphs with Density of States

Through extensive experiments using synthetic and real world data, we show that SCPD (a) achieves state-of-the-art performance, (b) is significantly faster than the state-of-the-art methods and can easily process millions of edges in a few CPU minutes, (c) can effectively tackle a large quantity of node attributes, additions or deletions and (d) discovers interesting events in large real world graphs.

Huang, S., Danovitch, J., Rabusseau, G., Rabbany, R. (2023). Fast and Attributed Change Detection on Dynamic Graphs with Density of States. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13935. Springer, Cham. https://doi.org/10.1007/978-3-031-33374-3_2 https://link.springer.com/chapter/10.1007/978-3-031-33374-3_2

The Surprising Performance of Simple Baselines for Misinformation Detection

We examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods

Kellin Pelrine, Jacob Danovitch, and Reihaneh Rabbany. 2021. The Surprising Performance of Simple Baselines for Misinformation Detection. In Proceedings of the Web Conference 2021 (WWW '21). Association for Computing Machinery, New York, NY, USA, 3432–3441. https://doi.org/10.1145/3442381.3450111 https://dl.acm.org/doi/abs/10.1145/3442381.3450111

ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents

We present Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities.

Pelrine, Kellin, et al. ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents.Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020. https://www.aclweb.org/anthology/2020.wnut-1.63/