Awesome Temporal Graph Learning is a collection of SOTA, novel temporal graph learning methods (papers, codes, and datasets).
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Updated
Jan 22, 2024
Awesome Temporal Graph Learning is a collection of SOTA, novel temporal graph learning methods (papers, codes, and datasets).
Deep and conventional community detection related papers, implementations, datasets, and tools.
Final project for Social Network Mining(DATA130007) in Fudan university
ANRL: Attributed Network Representation Learning via Deep Neural Networks(IJCAI-2018)
Network representation learning technique using structure and attributes of information networks.
A deep representation on heterogeneous drug network, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients.
Parallelized Binary embedding GENerator for Attributed graphs
Codes and data for the paper entitled "Learning representations to predict intermolecular interactions on large-scale heterogeneous molecular association network"
BioERP: a biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions.
OpenANE: the first Open source framework specialized in Attributed Network Embedding. The related paper was accepted by Neurocomputing. https://doi.org/10.1016/j.neucom.2020.05.080
Attributed Biased Random Walks (ABRW) is an Attributed Network Embedding method
2019 Biendata竞赛平台“OAG–WhoIsWho 同名消歧竞赛 赛道一”消歧比赛,第一名解决方案
This repository contains the tensorflow implementation of "GNE: A deep learning framework for gene network inference by aggregating biological information"
Codes for our SIGIR'20 paper "BiANE: Bipartite Attributed Network Embedding".
The Implementation of "Deep Recursive Network Embedding with Regular Equivalence"(KDD 2018)
[ICDM 2020] Deep Semantic Network Representation
Source code and dataset for IJCAI 2019 paper "ProNE: Fast and Scalable Network Representation Learning"
Code for the ICDM 2019 Paper "RiWalk: Fast Structural Node Embedding via Role Identification".
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