Measuring Over-smoothing beyond Dirichlet energy

A new paper presents advancements in universal graph prompt tuning for Graph Neural Networks (GNNs), emphasizing a theoretical foundation that allows for adaptability across various pre-training strategies. The authors argue that previous selective node-based tuning methods compromise this foundation, advocating for a more inclusive approach that applies prompts to all nodes.

Recent advancements in Graph Neural Networks (GNNs) have highlighted the need for improved explainability in these complex models. Current Explainable AI (XAI) methods often struggle to clarify the intricate relationships within graph structures, which can hinder their effectiveness in various applications. This research aims to enhance understanding through conceptual and structural analyses, addressing the limitations of existing approaches.

A new study introduces FIT-GNN, a method aimed at enhancing the scalability of Graph Neural Networks (GNNs) by reducing computational costs during the inference phase through graph coarsening techniques. The approach utilizes Extra Nodes and Cluster Nodes to achieve significant improvements in inference time across various benchmark datasets.

A new study introduces the concept of graph unlearning inversion attacks, challenging the assumption that sensitive data removed from Graph Neural Networks (GNNs) cannot be reconstructed. The research presents TrendAttack, a method that identifies vulnerabilities in unlearned GNNs by exploiting drops in model confidence near unlearned edges.

A recent study investigates the capacity of Graph Neural Networks (GNNs) to learn link heuristics for link prediction, revealing limitations in their ability to effectively learn structural information from common neighbors due to the set-based pooling method used in neighborhood aggregation. The research also indicates that trainable node embeddings can enhance GNN performance, particularly in denser graphs.

Twisted Convolutional Networks (TCNs) have been introduced as a new deep learning architecture designed for classifying one-dimensional data with arbitrary feature order and minimal spatial relationships. This innovative approach combines subsets of input features through multiplicative and pairwise interaction mechanisms, enhancing feature interactions that traditional convolutional methods often overlook.

A new method called DDFI (Diverse and Distribution-aware Missing Feature Imputation) has been introduced to enhance the imputation of missing node features in Graph Neural Networks (GNNs). This method addresses significant challenges such as over-smoothing and the limitations of feature propagation in disconnected graphs, making it particularly relevant for real-world applications where incomplete data is common.

Recent advancements in Graph Neural Networks (GNNs) have highlighted the challenge of enabling these models to 'forget' specific information, particularly in light of privacy regulations like the GDPR. A new approach proposes a transparent verification method for GNN unlearning, utilizing explainability metrics to assess whether designated data has been effectively removed from the model.