Global explanation supervision for Graph Neural Networks

Global explanation supervision for Graph Neural Networks - Frontiers

We propose the Global GNN Explanation Supervision (GGNES) technique which uses a basic trained GNN and a global extension of the loss function used in the GNES ...

Global explanation supervision for Graph Neural Networks - PMC

With the increasing popularity of Graph Neural Networks (GNNs) for predictive tasks on graph structured data, research on their ...

Global explanation supervision for Graph Neural Networks - PubMed

With the increasing popularity of Graph Neural Networks (GNNs) for predictive tasks on graph structured data, research on their ...

(PDF) Global explanation supervision for Graph Neural Networks

PDF | With the increasing popularity of Graph Neural Networks (GNNs) for predictive tasks on graph structured data, research on their ...

Global Counterfactual Explainer for Graph Neural Networks

Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to ...

On Data-Aware Global Explainability of Graph Neural Networks

We propose DAG-Explainer in this work aiming for global explainability. Specifically, we observe three properties of superior explanations for a pretrained GNN.

Explainable Graph Neural Networks: An Application to Open ... - MDPI

The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is ...

Global explanation supervision for Graph Neural Networks - CoLab

With the increasing popularity of Graph Neural Networks (GNNs) for predictive tasks on graph structured data, research on their ...

Graph neural networks: A review of methods and applications

As a unique non-Euclidean data structure for machine learning, graph analysis focuses on tasks such as node classification, link prediction, and clustering.

SelfGNN: Self-supervised Graph Neural Networks without explicit ...

Real world data is mostly unlabeled or only few instances are la- beled. Manually labeling data is a very expensive and daunting task. This calls for ...

Graph Neural Networks - Iowa State Online

Registration Opens Soon! Master Graph Neural Networks: From Basics to Real-World Applications. Course Description ... Self-Supervised Learning; Parallelism ...

Asymmetric Self-Supervised Graph Neural Networks - IEEE Xplore

... graph analysis to incorporate such one-way information passing. We define an ... Extensive experiments on multiple real-world directed graph datasets ...

SLAPS: Self-Supervision Improves Structure Learning for Graph...

Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications.

[D] Why I'm Lukewarm on Graph Neural Networks - Reddit

This is a simple and compact layout which can be good for analysis. The problem compared to CSR Graphs is some seek operations are slower. Say ...

Category-aware self-supervised graph neural network for session ...

For ease of explanation, we design the following two ... Global context enhanced graph neural networks for session-based recommendation.

Learning General Optimal Policies with Graph Neural Networks

... description logic grammar. At the same time, most description logics ... Proceedings of the International Conference on Automated ...

GTC: GNN-Transformer co-contrastive learning for self-supervised ...

In recent years, the advent of Graph Neural Networks (GNNs) has expanded the application scope of deep learning to graph analysis tasks. The ...

awesome-self-supervised-gnn/README.md at master - GitHub

... Graph Learning Framework with Global Self-Supervision [paper] ... [NeurIPS 2021] From Canonical Correlation Analysis to Self-supervised Graph Neural Networks [ ...

Graph Adversarial Self-Supervised Learning - NIPS papers

How powerful are graph neural networks. In International Conference on Learning Representations, 2018. [6] Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro ...

SIGIR 2024 T3.2 [fp] SelfGNN: Self-Supervised Graph ... - YouTube

Sequential Recommendation (T3.2) [fp] SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation - Authors: Yuxi Liu, ...