289 citations · 488 across the 6 of their papers we have counts for
7 papers
Spectral Graph Complexity
Anton Tsitsulin, Davide Mottin, Panagiotis Karras +2
We introduce a spectral notion of graph complexity derived from the Weyl's law. We experimentally demonstrate its correlation to how well the graph can be embedded in a low-dimensi…
GRASP: Graph Alignment through Spectral Signatures
Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva +3
What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinfo…
FREDE: Anytime Graph Embeddings
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin +3
Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks. As such embeddings rely, explicitly or impl…
The Shape of Data: Intrinsic Distance for Data Distributions
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin +4
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network…
SGR: Self-Supervised Spectral Graph Representation Learning
Anton Tsitsulin, Davide Mottin, Panagiotis Karras +2
Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the…
NetLSD: Hearing the Shape of a Graph
Anton Tsitsulin, Davide Mottin, Panagiotis Karras +2
Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its com…
VERSE: Versatile Graph Embeddings from Similarity Measures
Anton Tsitsulin, Davide Mottin, Panagiotis Karras +1
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addres…