289 citations · 539 across the 10 of their papers we have counts for
10 papers
On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification
Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen +3
In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset…
RODD: Robust Outlier Detection in Data Cubes
Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller +2
Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cube…
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…
Graph Clustering with Graph Neural Networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi +1
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised pr…
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…
Notable Characteristics Search through Knowledge Graphs
Davide Mottin, Bastian Grasnick, Axel Kroschk +2
Query answering routinely employs knowledge graphs to assist the user in the search process. Given a knowledge graph that represents entities and relationships among them, one aims…