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…
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…
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-…
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…