22 papers with code ·
Graphs

No evaluation results yet. Help compare methods by
submit
evaluation metrics.

Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI.

We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations.

We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs.

#4 best model for Graph Classification on NEURON-Average

Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science.

Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems.

Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING TOPOLOGICAL DATA ANALYSIS

We propose to apply the mapper construction--a popular tool in topological data analysis--to graph visualization, which provides a strong theoretical basis for summarizing network data while preserving their core structures.

The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.

#33 best model for Graph Classification on PROTEINS

While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.

Understanding how neural networks learn remains one of the central challenges in machine learning research.