The field of graph neural networks (GNNs) and causal inference is rapidly evolving, with recent developments focusing on improving robustness, efficiency, and interpretability. Researchers are exploring new architectures, such as geometric multi-color message-passing GNNs, and physics-informed GNNs, to enhance performance in various applications, including node classification, graph generation, and causal discovery. Furthermore, there is a growing interest in integrating GNNs with other techniques, such as knowledge distillation, transfer learning, and probabilistic reasoning, to improve their capabilities. Noteworthy papers in this area include 'Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors', which proposes a novel solution to bolster GNN resilience, and 'From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery', which introduces a probabilistic framework for causal discovery using GNNs. These advancements have significant implications for various fields, including computer vision, natural language processing, and decision-making.
Advancements in Graph Neural Networks and Causal Inference
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Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors
Even Faster Simulations with Flow Matching: A Study of Zero Degree Calorimeter Responses
Geometric Multi-color Message Passing Graph Neural Networks for Blood-brain Barrier Permeability Prediction
ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
Research on the application of graph data structure and graph neural network in node classification/clustering tasks
Joint Feature and Output Distillation for Low-complexity Acoustic Scene Classification
Integrating Activity Predictions in Knowledge Graphs
Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing
From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery
BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool
Personalized Treatment Effect Estimation from Unstructured Data
Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation
An ontological analysis of risk in Basic Formal Ontology
Digitalizing Uncertain Information
The Human Capital Ontology (Extended Abstract)
Exploring Adaptive Structure Learning for Heterophilic Graphs
Parallel PLL on DAGs
Torque-based Graph Surgery:Enhancing Graph Neural Networks with Hierarchical Rewiring
Hybrid Causal Identification and Causal Mechanism Clustering
A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data
Teach Me to Trick: Exploring Adversarial Transferability via Knowledge Distillation
Scalable Generative Modeling of Weighted Graphs
Incorporating structural uncertainty in causal decision making