Anton Tsitsulin

According to our database, Anton Tsitsulin authored at least 28 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Let Your Graph Do the Talking: Encoding Structured Data for LLMs.

Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Seyed Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow

CoRR, 2024

2023

GRASP: Scalable Graph Alignment by Spectral Corresponding Functions.

Judith Hermanns, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva, Alexander Frederiksen Kyster, Simon Nielsen, Alexander M. Bronstein, Davide Mottin, Panagiotis Karras

ACM Trans. Knowl. Discov. Data, May, 2023

Graph Clustering with Graph Neural Networks.

Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

J. Mach. Learn. Res., 2023

The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure.

Anton Tsitsulin, Bryan Perozzi

CoRR, 2023

UGSL: A Unified Framework for Benchmarking Graph Structure Learning.

Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Seyed Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi

CoRR, 2023

Examining the Effects of Degree Distribution and Homophily in Graph Learning Models.

Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi

CoRR, 2023

Unsupervised Embedding Quality Evaluation.

Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi

Proceedings of the Topological, 2023

Graph Neural Networks in TensorFlow.

Bryan Perozzi, Sami Abu-El-Haija, Anton Tsitsulin

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

HUGE: Huge Unsupervised Graph Embeddings with TPUs.

Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023).

Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022

On Classification Thresholds for Graph Attention with Edge Features.

Kimon Fountoulakis, Dake He, Silvio Lattanzi, Bryan Perozzi, Anton Tsitsulin, Shenghao Yang

CoRR, 2022

TF-GNN: Graph Neural Networks in TensorFlow.

Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter W. Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Silvio Lattanzi, André Linhares, Brandon A. Mayer, Vahab S. Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi

CoRR, 2022

Tackling Provably Hard Representative Selection via Graph Neural Networks.

Seyed Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab S. Mirrokni

CoRR, 2022

Synthetic Graph Generation to Benchmark Graph Learning.

Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi

CoRR, 2022

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank.

Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GraphWorld: Fake Graphs Bring Real Insights for GNNs.

John Palowitch, Anton Tsitsulin, Brandon A. Mayer, Bryan Perozzi

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Similarities and Representations of Graph Structures.

Anton Tsitsulin

PhD thesis, 2021

FREDE: Anytime Graph Embeddings.

Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan V. Oseledets, Emmanuel Müller

Proc. VLDB Endow., 2021

GRASP: Graph Alignment Through Spectral Signatures.

Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva, Alexander M. Bronstein, Davide Mottin, Panagiotis Karras

Proceedings of the Web and Big Data - 5th International Joint Conference, 2021

2020

InstantEmbedding: Efficient Local Node Representations.

Stefan Postavaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida, Yingtao Tian, Silvio Lattanzi, Bryan Perozzi

CoRR, 2020

FREDE: Linear-Space Anytime Graph Embeddings.

Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan V. Oseledets, Emmanuel Müller

CoRR, 2020

Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs.

Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

The Shape of Data: Intrinsic Distance for Data Distributions.

Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Intrinsic Multi-scale Evaluation of Generative Models.

Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller

CoRR, 2019

Spectral Graph Complexity.

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller

Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

2018

SGR: Self-Supervised Spectral Graph Representation Learning.

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller

CoRR, 2018

VERSE: Versatile Graph Embeddings from Similarity Measures.

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

NetLSD: Hearing the Shape of a Graph.

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018