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Today's Graph Neural Networks Research Top Papers
Wednesday, November 5, 2025
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Proposes a Graph Neural Network (GNN) approach for human simulation, matching or surpassing LLM baselines on choice-among-discrete-options tasks. Demonstrates GNNs can be orders of magnitude smaller than LLMs while achieving comparable performance.
Proposes a GNN-based approach for Handwritten Mathematical Expression recognition by modeling expressions as graphs. Uses a deep BLSTM for initial graph formation, refined by GNN link prediction for structure recognition.
Combines causal inference with graph neural networks to address brittleness and discrimination in healthcare AI. Aims to learn causal mechanisms rather than just statistical associations for improved reliability and interpretability.
Utilizes Graph Neural Networks to predict interspecies interactions in microbial communities using monoculture growth capabilities, interactions, and phylogeny data. Aims to capture critical factors for community structure and activity.
Develops secure inference protocols for Graph Neural Networks (GNNs) and graph-structured data in privacy-critical environments. Addresses the underexplored challenge of securing GNNs, focusing on high-performance protocols.
Proposes an influence-aware causal autoencoder network for node importance ranking in complex networks. Aims to design node importance without direct reliance on network topology, addressing privacy concerns and improving generalization.
Derives insights from user text descriptions to build a federated recommendation system based on graph structures. Addresses data localization challenges in federated learning by capturing global user relationships.
Introduces a novel dynamic variational graph model for link prediction in global food trade networks. Captures temporal patterns using momentum structural memory and Bayesian optimization to model evolving network structures.
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