Chenyi Zi
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View article: UniGAD: Unifying Multi-level Graph Anomaly Detection
UniGAD: Unifying Multi-level Graph Anomaly Detection Open
Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object type (node, edge, graph, etc.) and often overlook the inherent…
View article: ProG: A Graph Prompt Learning Benchmark
ProG: A Graph Prompt Learning Benchmark Open
Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and f…
View article: Deep Reinforcement Learning for Modelling Protein Complexes
Deep Reinforcement Learning for Modelling Protein Complexes Open
AlphaFold can be used for both single-chain and multi-chain protein structure prediction, while the latter becomes extremely challenging as the number of chains increases. In this work, by taking each chain as a node and assembly actions a…
View article: Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment Open
Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupervised learning, ar…