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Jingcan Duan

2 accepted papers

2023

Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

AAAI 2023technical

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention…

2023

Hard Sample Aware Network for Contrastive Deep Graph Clustering

AAAI 2023technical

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing…