Tool to convert datasets from "Benchmark Data Sets for Graph Kernels" (K. Kersting et al., 2016) into a format suitable for deep learning research.

NAG-FS (Network Atlas-Guided Feature Selection) for a fast and accurate graph data classification.

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations [AAAI-2020]

Zabbix Graphs Bottleneck Classification automates bottleneck analysis in network infrastructure using deep learning and the Zabbix monitoring system. It quickly identifies and classifies bottlenecks, enabling proactive network management and optimization.

A repo for baseline of graph pooling.

A Julia package for kernel functions on graphs

One-representative shot learning for graph classification.

Signed Whole Graph Embeddings

Knowledge-Aware ICD Coding.

Software implementation of Pyramidal Reservoir Graph Neural Networks

Few-Shot Graph Classification via distance metric learning.

Streamlit App for Node and Graph Classification and Explainability

Pattern Mining for the Classification of Public Procurement Fraud

repo for learning graph neural network

Re-implementation of G-Mixup: Graph Data Augmentation for Graph Classification

A package for downloading and working with graph datasets

Project for the MVA course: Machine learning with kernel methods

This is the code of the paper Breaking the Expressive Bottleneck of Graph Neural Networks.

This project uses graph theory to represent and navigate maps, providing a graphical user interface for visualization and employing Dijkstra's algorithm for finding the shortest paths between intersections. It supports map visualization, pathfinding, and interactive navigation through a command-line application and Swing-based interface.