Anton Tsitsulin

Orcid: 0000-0001-5519-7961

According to our database1, Anton Tsitsulin authored at least 34 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Bibliography

2024
Gemma 2: Improving Open Language Models at a Practical Size.
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CoRR, 2024

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights.
CoRR, 2024

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning.
CoRR, 2024

Understanding Transformer Reasoning Capabilities via Graph Algorithms.
CoRR, 2024

Don't Forget to Connect! Improving RAG with Graph-based Reranking.
CoRR, 2024

Let Your Graph Do the Talking: Encoding Structured Data for LLMs.
CoRR, 2024

Graph Reasoning with LLMs (GReaL).
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023
GRASP: Scalable Graph Alignment by Spectral Corresponding Functions.
ACM Trans. Knowl. Discov. Data, May, 2023

Tackling Provably Hard Representative Selection via Graph Neural Networks.
Trans. Mach. Learn. Res., 2023

Graph Clustering with Graph Neural Networks.
J. Mach. Learn. Res., 2023

The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure.
CoRR, 2023

UGSL: A Unified Framework for Benchmarking Graph Structure Learning.
CoRR, 2023

Examining the Effects of Degree Distribution and Homophily in Graph Learning Models.
CoRR, 2023

Unsupervised Embedding Quality Evaluation.
Proceedings of the Topological, 2023

Graph Neural Networks in TensorFlow.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

HUGE: Huge Unsupervised Graph Embeddings with TPUs.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023).
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022
On Classification Thresholds for Graph Attention with Edge Features.
CoRR, 2022

TF-GNN: Graph Neural Networks in TensorFlow.
CoRR, 2022

Synthetic Graph Generation to Benchmark Graph Learning.
CoRR, 2022

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GraphWorld: Fake Graphs Bring Real Insights for GNNs.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021
Similarities and Representations of Graph Structures.
PhD thesis, 2021

FREDE: Anytime Graph Embeddings.
Proc. VLDB Endow., 2021

GRASP: Graph Alignment Through Spectral Signatures.
Proceedings of the Web and Big Data - 5th International Joint Conference, 2021

2020
InstantEmbedding: Efficient Local Node Representations.
CoRR, 2020

FREDE: Linear-Space Anytime Graph Embeddings.
CoRR, 2020

Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs.
Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

The Shape of Data: Intrinsic Distance for Data Distributions.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Intrinsic Multi-scale Evaluation of Generative Models.
CoRR, 2019

Spectral Graph Complexity.
Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

2018
SGR: Self-Supervised Spectral Graph Representation Learning.
CoRR, 2018

VERSE: Versatile Graph Embeddings from Similarity Measures.
Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

NetLSD: Hearing the Shape of a Graph.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018


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