NetLSD: Hearing the Shape of a Graph. - Anton Tsitsulin; Davide Mottin; Panagiotis Karras; Alexander M. Bronstein; Emmanuel Müller (KDD, 2018)

VERSE: Versatile Graph Embeddings from Similarity Measures. - Anton Tsitsulin; Davide Mottin; Panagiotis Karras; Emmanuel Müller (WWW, 2018)

SGR: Self-Supervised Spectral Graph Representation Learning. - Anton Tsitsulin; Davide Mottin; Panagiotis Karras; Alexander M. Bronstein; Emmanuel Müller (CoRR, 2018)

Spectral Graph Complexity. - Anton Tsitsulin; Davide Mottin; Panagiotis Karras; Alexander M. Bronstein; Emmanuel Müller (WWW (Companion Volume), 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)

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 (ICLR, 2020)

Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. - Anton Tsitsulin; Marina Munkhoeva; Bryan Perozzi (WWW, 2020)

FREDE: Linear-Space Anytime Graph Embeddings. - Anton Tsitsulin; Marina Munkhoeva; Davide Mottin; Panagiotis Karras; Ivan V. Oseledets; Emmanuel Müller (CoRR, 2020)

InstantEmbedding: Efficient Local Node Representations. - Stefan Postavaru; Anton Tsitsulin; Filipe Miguel Gonçalves de Almeida; Yingtao Tian; Silvio Lattanzi; Bryan Perozzi (CoRR, 2020)

GRASP: Graph Alignment Through Spectral Signatures. - Judith Hermanns; Anton Tsitsulin; Marina Munkhoeva; Alexander M. Bronstein; Davide Mottin; Panagiotis Karras (APWeb/WAIM (1), 2021)

FREDE: Anytime Graph Embeddings. - Anton Tsitsulin; Marina Munkhoeva; Davide Mottin; Panagiotis Karras; Ivan V. Oseledets; Emmanuel Müller (Proc. VLDB Endow., 2021)

Similarities and Representations of Graph Structures. - Anton Tsitsulin (2021)

GraphWorld: Fake Graphs Bring Real Insights for GNNs. - John Palowitch; Anton Tsitsulin; Brandon A. Mayer; Bryan Perozzi (KDD, 2022)

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. - Alessandro Epasto; Vahab Mirrokni; Bryan Perozzi; Anton Tsitsulin; Peilin Zhong (NeurIPS, 2022)

Synthetic Graph Generation to Benchmark Graph Learning. - Anton Tsitsulin; Benedek Rozemberczki; John Palowitch; Bryan Perozzi (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; et al. (CoRR, 2022)

On Classification Thresholds for Graph Attention with Edge Features. - Kimon Fountoulakis; Dake He; Silvio Lattanzi; Bryan Perozzi; Anton Tsitsulin; Shenghao Yang 002 (CoRR, 2022)

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). - Jiaqi Ma 001; Jiong Zhu; Yuxiao Dong; Danai Koutra; Jingrui He; Qiaozhu Mei; Anton Tsitsulin; Xingjian Zhang 002; Marinka Zitnik (KDD, 2023)

HUGE: Huge Unsupervised Graph Embeddings with TPUs. - Brandon A. Mayer; Anton Tsitsulin; Hendrik Fichtenberger; Jonathan Halcrow; Bryan Perozzi (KDD, 2023)

Graph Neural Networks in TensorFlow. - Bryan Perozzi; Sami Abu-El-Haija; Anton Tsitsulin (KDD, 2023)

Unsupervised Embedding Quality Evaluation. - Anton Tsitsulin; Marina Munkhoeva; Bryan Perozzi (TAG-ML, 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)

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)

The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure. - Anton Tsitsulin; Bryan Perozzi (CoRR, 2023)

Graph Clustering with Graph Neural Networks. - Anton Tsitsulin; John Palowitch; Bryan Perozzi; Emmanuel Müller (J. Mach. Learn. Res., 2023)

Tackling Provably Hard Representative Selection via Graph Neural Networks. - Mehran Kazemi; Anton Tsitsulin; Hossein Esfandiari; MohammadHossein Bateni; Deepak Ramachandran; Bryan Perozzi; Vahab Mirrokni (Trans. Mach. Learn. Res., 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, 2023)

Graph Reasoning with LLMs (GReaL). - Anton Tsitsulin; Bryan Perozzi; Bahare Fatemi; Jonathan J. Halcrow (KDD, 2024)

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. - Zhikai Chen; Haitao Mao; Jingzhe Liu; Yu Song; Bingheng Li; Wei Jin 009; Bahare Fatemi; Anton Tsitsulin; Bryan Perozzi; Hui Liu 031; Jiliang Tang (NeurIPS, 2024)

Understanding Transformer Reasoning Capabilities via Graph Algorithms. - Clayton Sanford; Bahare Fatemi; Ethan Hall; Anton Tsitsulin; Mehran Kazemi; Jonathan Halcrow; Bryan Perozzi; Vahab Mirrokni (NeurIPS, 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)

Don't Forget to Connect! Improving RAG with Graph-based Reranking. - Jialin Dong; Bahare Fatemi; Bryan Perozzi; Lin F. Yang; Anton Tsitsulin (CoRR, 2024)

Gemma 2: Improving Open Language Models at a Practical Size. - 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; et al. (CoRR, 2024)

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning. - Bahare Fatemi; Mehran Kazemi; Anton Tsitsulin; Karishma Malkan; Jinyeong Yim; John Palowitch; Sungyong Seo; Jonathan Halcrow; Bryan Perozzi (ICLR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)