VERSE: Versatile Graph Embeddings from Similarity Measures A Tsitsulin, D Mottin, P Karras, E Müller World Wide Web Conference, 539-548, 2018 | 313 | 2018 |
Graph clustering with graph neural networks A Tsitsulin, J Palowitch, B Perozzi, E Müller Journal of Machine Learning Research 24, 1-21, 2023 | 197 | 2023 |
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 | 180 | 2018 |
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 | 62 | 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 | 42 | 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 | 37* | 2021 |
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 | 24* | 2022 |
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 | 22 | 2020 |
Synthetic Graph Generation to Benchmark Graph Learning A Tsitsulin, B Rozemberczki, J Palowitch, B Perozzi arXiv preprint arXiv:2204.01376, 2022 | 18 | 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 | 17 | 2020 |
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank A Epasto, V Mirrokni, B Perozzi, A Tsitsulin, P Zhong NeurIPS, 2022 | 14 | 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 | 8 | 2021 |
SGR: Self-Supervised Spectral Graph Representation Learning A Tsitsulin, D Mottin, P Karras, A Bronstein, E Müller arXiv preprint arXiv:1811.06237, 2018 | 8 | 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 | 5 | 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 | 4 | 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 | 3 | 2024 |
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 | 3 | 2023 |
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models M Yasir, J Palowitch, A Tsitsulin, L Tran-Thanh, B Perozzi arXiv preprint arXiv:2307.08881, 2023 | 3 | 2023 |
Unsupervised embedding quality evaluation A Tsitsulin, M Munkhoeva, B Perozzi Topological, Algebraic and Geometric Learning Workshops 2023, 169-188, 2023 | 2 | 2023 |
GRASP: Scalable Graph Alignment by Spectral Corresponding Functions J Hermanns, K Skitsas, A Tsitsulin, M Munkhoeva, A Kyster, S Nielsen, ... ACM Transactions on Knowledge Discovery from Data 17 (4), 1-26, 2023 | 2 | 2023 |