3 citations · 3 across the 1 of their papers we have counts for
6 papers
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…
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…
Publishing Microdata with a Robust Privacy Guarantee
Jianneng Cao, Panagiotis Karras
Today, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devis…