Anton Tsitsulin
Anton Tsitsulin
Research Scientist, Google Research
Verified email at - Homepage
Cited by
Cited by
VERSE: Versatile Graph Embeddings from Similarity Measures
A Tsitsulin, D Mottin, P Karras, E Müller
World Wide Web Conference, 539-548, 2018
NetLSD: hearing the shape of a graph
A Tsitsulin, D Mottin, P Karras, A Bronstein, E Müller
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
Graph clustering with graph neural networks
A Tsitsulin, J Palowitch, B Perozzi, E Müller
Journal of Machine Learning Research 24, 1-21, 2023
Graphworld: Fake graphs bring real insights for gnns
J Palowitch, A Tsitsulin, B Mayer, B Perozzi
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
The Shape of Data: Intrinsic Distance for Data Distributions
A Tsitsulin, M Munkhoeva, D Mottin, P Karras, A Bronstein, I Oseledets, ...
ICLR 2020: Proceedings of the International Conference on Learning …, 2020
FREDE: Anytime graph embeddings
A Tsitsulin, M Munkhoeva, D Mottin, P Karras, IV Oseledets, E Müller
Proceedings of the VLDB Endowment 14 (6), 1102-1110, 2021
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
A Tsitsulin, M Munkhoeva, B Perozzi
Proceedings of The Web Conference 2020, 2697-2703, 2020
Tf-gnn: Graph neural networks in tensorflow
O Ferludin, A Eigenwillig, M Blais, D Zelle, J Pfeifer, A Sanchez-Gonzalez, ...
arXiv preprint arXiv:2207.03522, 2022
InstantEmbedding: Efficient Local Node Representations
Ş Postăvaru, A Tsitsulin, FMG de Almeida, Y Tian, S Lattanzi, B Perozzi
arXiv preprint arXiv:2010.06992, 2020
Synthetic Graph Generation to Benchmark Graph Learning
A Tsitsulin, B Rozemberczki, J Palowitch, B Perozzi
arXiv preprint arXiv:2204.01376, 2022
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
A Epasto, V Mirrokni, B Perozzi, A Tsitsulin, P Zhong
NeurIPS, 2022
Grasp: Graph alignment through spectral signatures
J Hermanns, A Tsitsulin, M Munkhoeva, A Bronstein, D Mottin, P Karras
Web and Big Data: 5th International Joint Conference, APWeb-WAIM 2021 …, 2021
SGR: Self-Supervised Spectral Graph Representation Learning
A Tsitsulin, D Mottin, P Karras, A Bronstein, E Müller
arXiv preprint arXiv:1811.06237, 2018
Spectral Graph Complexity
A Tsitsulin, D Mottin, P Karras, A Bronstein, E Müller
Companion Proceedings of The 2019 World Wide Web Conference, 308-309, 2019
On Classification Thresholds for Graph Attention with Edge Features
K Fountoulakis, D He, S Lattanzi, B Perozzi, A Tsitsulin, S Yang
arXiv preprint arXiv:2210.10014, 2022
UGSL: A Unified Framework for Benchmarking Graph Structure Learning
B Fatemi, S Abu-El-Haija, A Tsitsulin, M Kazemi, D Zelle, N Bulut, ...
arXiv preprint arXiv:2308.10737, 2023
Unsupervised Embedding Quality Evaluation
A Tsitsulin, M Munkhoeva, B Perozzi
arXiv preprint arXiv:2305.16562, 2023
Tackling Provably Hard Representative Selection via Graph Neural Networks
SM Kazemi, A Tsitsulin, H Esfandiari, MH Bateni, D Ramachandran, ...
arXiv preprint arXiv:2205.10403, 2022
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
B Perozzi, B Fatemi, D Zelle, A Tsitsulin, M Kazemi, R Al-Rfou, J Halcrow
arXiv preprint arXiv:2402.05862, 2024
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure
A Tsitsulin, B Perozzi
arXiv preprint arXiv:2312.04762, 2023
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