Anton Tsitsulin

ACM Trans. Knowl. Discov. Data, May, 2023

Tackling Provably Hard Representative Selection via Graph Neural Networks.

[BibT_eX]

[DOI]

Mehran Kazemi

Hossein Esfandiari

MohammadHossein Bateni

Deepak Ramachandran

Vahab Mirrokni

Trans. Mach. Learn. Res., 2023

Graph Clustering with Graph Neural Networks.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

UGSL: A Unified Framework for Benchmarking Graph Structure Learning.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Unsupervised Embedding Quality Evaluation.

[BibT_eX]

[DOI]

Marina Munkhoeva

Proceedings of the Topological, 2023

Graph Neural Networks in TensorFlow.

[BibT_eX]

[DOI]

Sami Abu-El-Haija

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

HUGE: Huge Unsupervised Graph Embeddings with TPUs.

[BibT_eX]

[DOI]

Brandon A. Mayer

Hendrik Fichtenberger

Jonathan Halcrow

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023).

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022

On Classification Thresholds for Graph Attention with Edge Features.

[BibT_eX]

[DOI]

CoRR, 2022

TF-GNN: Graph Neural Networks in TensorFlow.

[BibT_eX]

[DOI]

CoRR, 2022

Synthetic Graph Generation to Benchmark Graph Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

PhD thesis, 2021

FREDE: Anytime Graph Embeddings.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2021

GRASP: Graph Alignment Through Spectral Signatures.

[BibT_eX]

[DOI]

Judith Hermanns

Marina Munkhoeva

Proceedings of the Web and Big Data - 5th International Joint Conference, 2021

2020

InstantEmbedding: Efficient Local Node Representations.

[BibT_eX]

[DOI]

Stefan Postavaru

Filipe Miguel Gonçalves de Almeida

Yingtao Tian

Silvio Lattanzi

CoRR, 2020

FREDE: Linear-Space Anytime Graph Embeddings.

[BibT_eX]

[DOI]

CoRR, 2020

Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs.

[BibT_eX]

[DOI]

Marina Munkhoeva

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.

[BibT_eX]

[DOI]

Ivan V. Oseledets

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Intrinsic Multi-scale Evaluation of Generative Models.

[BibT_eX]

[DOI]

Ivan V. Oseledets

CoRR, 2019

Spectral Graph Complexity.

[BibT_eX]

[DOI]

Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

2018

SGR: Self-Supervised Spectral Graph Representation Learning.

[BibT_eX]

[DOI]

CoRR, 2018

VERSE: Versatile Graph Embeddings from Similarity Measures.

[BibT_eX]

[DOI]

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

NetLSD: Hearing the Shape of a Graph.

[BibT_eX]

[DOI]