6 papers
Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection
Xudong Chen, Shengbo Gong, Lu Cheng +1
Conformal prediction (CP) provides distribution-free coverage guarantees and has emerged as a principled tool for uncertainty quantification. In edge-level fraud detection on tempo…
Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
Shengbo Gong, Xianfeng Tang, Qi He +2
Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs). However, advanced RAG systems f…
Higher-order Interaction Matters: Dynamic Hypergraph Neural Networks for Epidemic Modeling
Songyuan Liu, Shengbo Gong, Tianning Feng +3
The ongoing need for effective epidemic modeling has driven advancements in capturing the complex dynamics of infectious diseases. Traditional models, such as Susceptible-Infected-…
Scalable Graph Condensation with Evolving Capabilities
Shengbo Gong, Mohammad Hashemi, Juntong Ni +2
The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size. To mitigate these issues, Graph Condensation (GC)…
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
Shengbo Gong, Juntong Ni, Noveen Sachdeva +2
Graph condensation (GC) is an emerging technique designed to learn a significantly smaller graph that retains the essential information of the original graph. This condensed graph…
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
Mohammad Hashemi, Shengbo Gong, Juntong Ni +3
Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant…