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From the 1 of 12 linked papers with an AI index.

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20242026
most citedNodeImport: Imbalanced Node Classification with Node Importance Assessment

1 citations · 1 across the 2 of their papers we have counts for

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12 papers

cs.LG20261 cited

NodeImport: Imbalanced Node Classification with Node Importance Assessment

Nan Chen, Zemin Liu, Bryan Hooi +3

The paper proposes NodeImport, a framework that assesses node importance using a balanced meta-set to dynamically select valuable labeled, unlabeled, and synthetic nodes, improving…

cs.LG2026

Multi-Label Node Classification with Label Influence Propagation

Yifei Sun, Zemin Liu, Bryan Hooi +4

Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI…

cs.IR2026

Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion

Yuan Li, Jun Hu, Jiaxin Jiang +2

Multimodal data plays a critical role in web-based recommendation systems, where information from diverse modalities such as vision and text enhances representation learning. Howev…

cs.AI2026

EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning

Yufei He, Juncheng Liu, Zhiyuan Hu +9

Prevailing medical AI operates on an unrealistic ''one-shot'' model, diagnosing from a complete patient file. However, real-world diagnosis is an iterative inquiry where Clinicians…

cs.CL2026

Autonomous Chain-of-Thought Distillation for Graph-Based Fraud Detection

Yuan Li, Jun Hu, Bryan Hooi +2

Graph-based fraud detection on text-attributed graphs (TAGs) requires jointly modeling rich textual semantics and relational dependencies. However, existing LLM-enhanced GNN approa…

cs.LG2025

Echoless Label-Based Pre-computation for Memory-Efficient Heterogeneous Graph Learning

Jun Hu, Shangheng Chen, Yufei He +3

Heterogeneous Graph Neural Networks (HGNNs) are widely used for deep learning on heterogeneous graphs. Typical end-to-end HGNNs require repetitive message passing during training,…