| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 361 | 2024 |
| VERSE: Versatile Graph Embeddings from Similarity Measures A Tsitsulin, D Mottin, P Karras, E Müller World Wide Web Conference, 539-548, 2018 | 330 | 2018 |
| Graph clustering with graph neural networks A Tsitsulin, J Palowitch, B Perozzi, E Müller Journal of Machine Learning Research 24, 1-21, 2023 | 271 | 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 | 194 | 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 | 75 | 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 | 54 | 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 | 44 | 2022 |
| 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 | 42* | 2021 |
| Gemma 2: Improving Open Language Models at a Practical Size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 40 | 2024 |
| Synthetic Graph Generation to Benchmark Graph Learning A Tsitsulin, B Rozemberczki, J Palowitch, B Perozzi arXiv preprint arXiv:2204.01376, 2022 | 26* | 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 | 24 | 2020 |
| Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank A Epasto, V Mirrokni, B Perozzi, A Tsitsulin, P Zhong NeurIPS, 2022 | 21 | 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 | 20 | 2020 |
| 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 | 17 | 2024 |
| 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 | 11 | 2021 |
| SGR: Self-Supervised Spectral Graph Representation Learning A Tsitsulin, D Mottin, P Karras, A Bronstein, E Müller arXiv preprint arXiv:1811.06237, 2018 | 9 | 2018 |
| 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 | 7 | 2023 |
| 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 | 6 | 2023 |
| Understanding Transformer Reasoning Capabilities via Graph Algorithms C Sanford, B Fatemi, E Hall, A Tsitsulin, M Kazemi, J Halcrow, B Perozzi, ... arXiv preprint arXiv:2405.18512, 2024 | 5 | 2024 |
| 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 | 5 | 2023 |
Bryan PerozziGoogle ResearchVerified email at google.com
