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Found 3 papers, 2 papers with code
LocAgent: Graph-Guided LLM Agents for Code Localization
By parsing codebases into directed heterogeneous graphs, LocAgent creates a lightweight representation that captures code structures (files, classes, functions) and their dependencies (imports, invocations, inheritance), enabling LLM agents to effectively search and locate relevant entities through powerful multi-hop reasoning.
Learning Personalized Scoping for Graph Neural Networks under Heterophily
Increasing the GNN depth can expand the scope (i. e., receptive field), potentially finding homophily from the higher-order neighborhoods.
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation.
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