#Graph Neural Network

This list provides a diverse collection of research papers on fraud detection, sourced from leading conferences in disciplines like Network Science, Data Science, NLP, and AI. It highlights advanced techniques such as graph neural networks and semi-supervised learning, focusing on contemporary methods in fraud prevention. Researchers and professionals can access studies from conferences like ASONAM, KDD, CIKM, and IJCAI, gaining insights and practical applications to improve their fraud detection capabilities.

GFlowNet is an innovative modeling framework optimized for discrete, combinatorial objects like graphs. The library centers on node-by-node graph construction via a graph neural network, which provides per-node and per-graph outputs. It supports multiple GFN algorithms and facilitates training with both existing and generated data. This resource offers detailed examples for practical application, such as molecule generation, and is compatible with Python 3.9+. It is available for installation through PIP, enabling flexible management of dependencies.

This repository is a curated set of resources exploring Graph Neural Networks (GNNs) applied to traffic forecasting. It presents updated research findings and practical applications, including scholarly articles, associated repositories, and datasets for refined deep learning in traffic analysis. Designed for researchers and professionals, it aims to advance traffic prediction capabilities by employing state-of-the-art GNN techniques. Contributions to special issues are also welcomed, promoting ongoing innovation and teamwork within the domain.

Deploys Pytorch Geometric API for efficient analysis of large, dynamic heterogeneous graphs, providing flexible graph neural network solutions functional within varied computational setups. Key highlights include transformer-like convolutional layers and advanced sampling algorithms, perfectly suited for tasks like L2 classification on the Open Academic Graph dataset. Straightforward setup allows adaptation to diverse datasets through simple data interface modification, ensuring broad applicability and robust performance management.