jindi-tju Goto Github PK

algm

Source code for IJCAI23 "A Generalized Deep Markov Random Fields Framework for Fake News Detection"

amil

The source code of "Adversarial Mutual Information Learning for Network Embedding".

as-gcn

Source code of "ICDM21 - AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks"

bite-gcn

Source code of "WSDM21 - BiTe-GCN: A New GCN Architecture via Bidirectional Convolution of Topology and Features on Text-Rich Networks"

bm-gcn

The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".

cgmn

csdgcn

Source code for IJCAJ23 “Commonsense Knowledge Enhanced Sentiment Dependency Graph for Sarcasm Detection”

ergcn

ga-ggd

Source code for GA-GGD

hgca

Source code for TNNLS22 “Analyzing Heterogeneous Network with Missing Attributes by Unsupervised Contrastive Learning“

hgnn-ac

Source code of "WWW21 - Heterogeneous Graph Neural Network via Attribute Completion"

iaad

Source code for AAAI23 “Augmenting Affective Dependency Graph via Iterative Incongruity Graph Learning for Sarcasm Detection”

lgfa

Source code for KSEM23 “Local-Global Fusion Augmented Graph Contrastive Learning Based on Generative Models”

mrfasgcn

This is a TensorFlow implementation of MRFasGCN for the task of semi-supervised community detection.

paper_reading_summary

Summary of top conference papers related to the network, including representation learning, graph neural network, probability graph model, contrastive learning, etc.

rawgnn

t2-gnn

Source code for AAAI2023 "T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation"

trafformer

Source code for AAAI23 “Trafformer: Unify Time and Space in Traffic Prediction”

trustgnn

Source code for TNNLS23 "TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature"

u-gcn

Source code of "NeurIPS21 - Universal Graph Convolutional Networks"

ulgd

Source code for AAAI23 “Local-Global Defense against Unsupervised Adversarial Attacks on Graphs”