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2020 – today

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2024
[c13]
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Anton Tsitsulin, Bryan Perozzi, Bahare Fatemi, Jonathan J. Halcrow:
Graph Reasoning with LLMs (GReaL). KDD 2024: 6424-6425
[i27]
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Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Seyed Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow:
Let Your Graph Do the Talking: Encoding Structured Data for LLMs. CoRR abs/2402.05862 (2024)
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Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin:
Don't Forget to Connect! Improving RAG with Graph-based Reranking. CoRR abs/2405.18414 (2024)
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Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Seyed Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni:
Understanding Transformer Reasoning Capabilities via Graph Algorithms. CoRR abs/2405.18512 (2024)
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Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin, Karishma Malkan, Jinyeong Yim, John Palowitch, Sungyong Seo, Jonathan Halcrow, Bryan Perozzi:
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning. CoRR abs/2406.09170 (2024)
[i23]
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Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang:
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. CoRR abs/2406.10727 (2024)
[i22]
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  - journals/corr/abs-2408-00118
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Morgane Rivière, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozinska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucinska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju-yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjösund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus:
Gemma 2: Improving Open Language Models at a Practical Size. CoRR abs/2408.00118 (2024)
2023
[j4]
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Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. J. Mach. Learn. Res. 24: 127:1-127:21 (2023)
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Judith Hermanns, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva, Alexander Frederiksen Kyster, Simon Nielsen, Alexander M. Bronstein, Davide Mottin, Panagiotis Karras:
GRASP: Scalable Graph Alignment by Spectral Corresponding Functions. ACM Trans. Knowl. Discov. Data 17(4): 50:1-50:26 (2023)
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Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni:
Tackling Provably Hard Representative Selection via Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
[c12]
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Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi:
HUGE: Huge Unsupervised Graph Embeddings with TPUs. KDD 2023: 4638-4648
[c11]
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Bryan Perozzi, Sami Abu-El-Haija, Anton Tsitsulin:
Graph Neural Networks in TensorFlow. KDD 2023: 5786-5787
[c10]
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Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik:
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). KDD 2023: 5870-5871
[c9]
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Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Unsupervised Embedding Quality Evaluation. TAG-ML 2023: 169-188
[i21]
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Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Unsupervised Embedding Quality Evaluation. CoRR abs/2305.16562 (2023)
[i20]
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Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi:
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models. CoRR abs/2307.08881 (2023)
[i19]
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Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi:
HUGE: Huge Unsupervised Graph Embeddings with TPUs. CoRR abs/2307.14490 (2023)
[i18]
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Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Seyed Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi:
UGSL: A Unified Framework for Benchmarking Graph Structure Learning. CoRR abs/2308.10737 (2023)
[i17]
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Anton Tsitsulin, Bryan Perozzi:
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure. CoRR abs/2312.04762 (2023)
2022
[c8]
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John Palowitch, Anton Tsitsulin, Brandon A. Mayer, Bryan Perozzi:
GraphWorld: Fake Graphs Bring Real Insights for GNNs. KDD 2022: 3691-3701
[c7]
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Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS 2022
[i16]
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John Palowitch, Anton Tsitsulin, Brandon A. Mayer, Bryan Perozzi:
GraphWorld: Fake Graphs Bring Real Insights for GNNs. CoRR abs/2203.00112 (2022)
[i15]
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  - journals/corr/abs-2204-01376
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Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi:
Synthetic Graph Generation to Benchmark Graph Learning. CoRR abs/2204.01376 (2022)
[i14]
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  - journals/corr/abs-2205-10403
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Seyed Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab S. Mirrokni:
Tackling Provably Hard Representative Selection via Graph Neural Networks. CoRR abs/2205.10403 (2022)
[i13]
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  - journals/corr/abs-2207-03522
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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:
TF-GNN: Graph Neural Networks in TensorFlow. CoRR abs/2207.03522 (2022)
[i12]
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Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR abs/2207.06944 (2022)
[i11]
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Kimon Fountoulakis, Dake He, Silvio Lattanzi, Bryan Perozzi, Anton Tsitsulin, Shenghao Yang:
On Classification Thresholds for Graph Attention with Edge Features. CoRR abs/2210.10014 (2022)
[i10]
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
Spectral Graph Complexity. CoRR abs/2211.01434 (2022)
2021
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Anton Tsitsulin:
Similarities and Representations of Graph Structures. University of Bonn, Germany, 2021
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Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan V. Oseledets, Emmanuel Müller:
FREDE: Anytime Graph Embeddings. Proc. VLDB Endow. 14(6): 1102-1110 (2021)
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Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva, Alexander M. Bronstein, Davide Mottin, Panagiotis Karras:
GRASP: Graph Alignment Through Spectral Signatures. APWeb/WAIM (1) 2021: 44-52
[i9]
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Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva, Alex M. Bronstein, Davide Mottin, Panagiotis Karras:
GRASP: Graph Alignment through Spectral Signatures. CoRR abs/2106.05729 (2021)
2020
[c5]
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Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller:
The Shape of Data: Intrinsic Distance for Data Distributions. ICLR 2020
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Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. WWW 2020: 2697-2703
[i8]
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Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. CoRR abs/2003.01282 (2020)
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Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan V. Oseledets, Emmanuel Müller:
FREDE: Linear-Space Anytime Graph Embeddings. CoRR abs/2006.04746 (2020)
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Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. CoRR abs/2006.16904 (2020)
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Stefan Postavaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida, Yingtao Tian, Silvio Lattanzi, Bryan Perozzi:
InstantEmbedding: Efficient Local Node Representations. CoRR abs/2010.06992 (2020)

2010 – 2019

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2019
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
Spectral Graph Complexity. WWW (Companion Volume) 2019: 308-309
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Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller:
Intrinsic Multi-scale Evaluation of Generative Models. CoRR abs/1905.11141 (2019)
2018
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
NetLSD: Hearing the Shape of a Graph. KDD 2018: 2347-2356
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller:
VERSE: Versatile Graph Embeddings from Similarity Measures. WWW 2018: 539-548
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller:
VERSE: Versatile Graph Embeddings from Similarity Measures. CoRR abs/1803.04742 (2018)
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  - journals/corr/abs-1805-10712
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
NetLSD: Hearing the Shape of a Graph. CoRR abs/1805.10712 (2018)
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  - journals/corr/abs-1811-06237
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Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
SGR: Self-Supervised Spectral Graph Representation Learning. CoRR abs/1811.06237 (2018)

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