IEEE

A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Abstract: Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading to ...
IEEE

AutoGRN: An Automated Graph Neural Network Framework for Gene Regulatory Network Inference

Abstract: Gene regulatory network (GRN) inference is essential for understanding gene interactions that control biological functions and disease progression. Its goal is to uncover regulatory ...
GitHub

ras_retrieval-and-structuring_for_knowledge-intensive_llm_generation.md

This paper proposes RAS, a framework that dynamically constructs query-specific knowledge graphs at inference time for each input question. Through three stages—iterative retrieval planning, ...