EHCF

RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).

Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)

Resources of our survey paper "Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies"

This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

the Pedestrian_graph_plus implementation

[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction

Lab "Efficient Algorithms for Selected Problems" in summer term 2016 for University of Bonn

PyTorch Implementation of Context-Aware Sequential Model for Multi-Behaviour Recommendation https://arxiv.org/abs/2312.09684