Dual|discriminative Graph Neural Network for Imbalanced Graph ...

Dual-discriminative Graph Neural Network for Imbalanced Graph ...

In this paper, we propose a graph neural network for graph-level anomaly detection, namely iGAD. Specifically, an anomalous graph attribute-aware graph ...

Dual-discriminative Graph Neural Network for Imbalanced Graph ...

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset from normal graphs. Anomalous graphs represent a very few but ...

Dual-discriminative graph neural network for imbalanced graph ...

Dual-discriminative graph neural network for imbalanced graph-level anomaly detection. AUTHORs: Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, + 7 ...

Dual-discriminative Graph Neural Network for Imbalanced Graph ...

In this paper, we propose a graph neural network for graph-level anomaly detection, namely iGAD. Specifically, an anomalous graph attribute-aware graph ...

Supplementary Material: Dual-discriminative Graph Neural Network for

Imbalanced Graph-level Anomaly Detection". 492. A Algorithm. 493. This section contains the proof/explanation of Eq.

Dual-discriminative Graph Neural Network for Imbalanced Graph ...

... Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection". Choose a paper to build a graph: Search powered ...

Imbalanced Graph Classification with Multi-scale Oversampling ...

We introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter- ...

Imbalanced graph learning via mixed entropy minimization - Nature

... imbalanced datasets, thereby improving both their discriminative power and overall effectiveness. ... Graph neural network with curriculum ...

FelixDJC/Awesome-Graph-Anomaly-Detection - GitHub

CIKM 2021: Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning ... NeurIPS 2022: Dual-discriminative Graph Neural Network for Imbalanced ...

Addressing imbalance in graph datasets: Introducing GATE-GNN ...

Imbalanced data presents a formidable challenge in the field of graph neural networks (GNNs), particularly impacting the performance of node classification ...

Dual Graph Networks with Synthetic Oversampling for Imbalanced ...

IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification. In Proceedings of the 11th International ...

A Survey of Graph Neural Networks in Real world: Imbalance, Noise ...

Index Terms—Graph Neural Network, Imbalance, Noise, Privacy, Out-of-Distribution. ♢. 1 INTRODUCTION. GRAPH-STRUCTURED data, characterized by ...

Graph Neural Network with curriculum learning for imbalanced node ...

Additionally, the deep graph classifiers' accuracy is hampered by the dual constraints of data scarcity and class imbalance. Regrettably, these issues are ...

Awesome Literature on Imbalanced Learning on Graphs (ILoGs)

GraphSMOTE, GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks, WSDM 2021, PDF · PyTorch ; ImGAGN, ImGAGN: Imbalanced Network ...

Dual Cost-sensitive Graph Convolutional Network - IEEE Xplore

In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassification have equal loss and thus ...

An imbalanced learning method based on graph tran-smote for ...

Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to ...

Imbalanced Graph Classification via Graph-of-Graph Neural Networks

A novel framework, Graph-of-Graph Neural Networks (G2GNN), is introduced, which alleviates the graph imbalance issue by deriving extra supervision globally ...

DOS-GNN: Dual-Feature Aggregations with Over-Sampling for ...

... Dual-Feature Aggregations with Over-Sampling for Class ... GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks.

Multi-Class Imbalanced Graph Convolutional Network Learning - IJCAI

In this paper, we propose Dual-. Regularized Graph Convolutional Networks (DR-. GCN) to handle multi-class imbalanced graphs, where two types of ...

Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance

We present a new fraud detection model FRAUDRE based on Graph Neural Networks. Extensive experiments comparing eight up-to-date baselines on two real-world ...