Pinned Repositories
DCRS
DCRS: Encoding Node Diffusion Competence and Role Significance for Network Dismantling
DeepIM
DQN-UN-TL
DQN method to address Competitive Influence Maximization on Unknown Social Networks
example
test
graph_sample_rl
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling (official code repository)
hello-world
IVGD
MySubmarine
test
OpenNE_TF
An Open-Source Package for Network Embedding (NE)
review
Repository of scripts and data for the "Robustness and resilience of complex networks" paper by Oriol Artime, Marco Grassia, Manlio De Domenico, James P. Gleeson, Hernán A. Makse, Giuseppe Mangioni, Matjaž Perc and Filippo Radicchi, published at Nature Review Physics (2024). https://doi.org/10.1038/s42254-023-00676-y
saltwater-fish's Repositories
saltwater-fish/DCRS
DCRS: Encoding Node Diffusion Competence and Role Significance for Network Dismantling
saltwater-fish/DeepIM
saltwater-fish/DQN-UN-TL
DQN method to address Competitive Influence Maximization on Unknown Social Networks
saltwater-fish/example
test
saltwater-fish/graph_sample_rl
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling (official code repository)
saltwater-fish/hello-world
saltwater-fish/IVGD
saltwater-fish/MySubmarine
test
saltwater-fish/OpenNE_TF
An Open-Source Package for Network Embedding (NE)
saltwater-fish/review
Repository of scripts and data for the "Robustness and resilience of complex networks" paper by Oriol Artime, Marco Grassia, Manlio De Domenico, James P. Gleeson, Hernán A. Makse, Giuseppe Mangioni, Matjaž Perc and Filippo Radicchi, published at Nature Review Physics (2024). https://doi.org/10.1038/s42254-023-00676-y
saltwater-fish/RolX
An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)
saltwater-fish/saltwater-fish.github.io
saltwater-fish/SDNE
This is a implementation of SDNE (Structural Deep Network embedding)
saltwater-fish/Simple-Hr-System
一个简单的Hr管理系统
saltwater-fish/SLVAE
saltwater-fish/TGCN
The codes for the "Heterogeneous Reinforcement Learning for Defending Power Grids Against Attacks" paper submitted to APML journal