Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection

• URL: http://arxiv.org/abs/2312.09015v2
• Date: Thu, 4 Jan 2024 14:23:03 GMT
• Title: Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection
• Authors: William Leeney and Ryan McConville
• Abstract summary: Real-world benchmarks are perplexing due to the multitude of decisions influencing GNN evaluations. $W$ Randomness coefficient, based on the Wasserstein distance, is identified as providing the most robust assessment of randomness.
• Score: 4.358468367889626
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: (1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs. The identification of latent communities holds practical significance in various domains, from social networks to genomics. Current real-world performance benchmarks are perplexing due to the multitude of decisions influencing GNN evaluations for this task. (2) Three metrics are compared to assess the consistency of algorithm rankings in the presence of randomness. The consistency and quality of performance between the results under a hyperparameter optimisation with the default hyperparameters is evaluated. (3) The results compare hyperparameter optimisation with default hyperparameters, revealing a significant performance loss when neglecting hyperparameter investigation. A comparison of metrics indicates that ties in ranks can substantially alter the quantification of randomness. (4) Ensuring adherence to the same evaluation criteria may result in notable differences in the reported performance of methods for this task. The $W$ Randomness coefficient, based on the Wasserstein distance, is identified as providing the most robust assessment of randomness.

Related papers

• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks [40.95782849532316]
  We propose a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of centering data to graph data through novel graph anchoring strategies. Our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$Delta$UQ in obtaining reliable estimates.
  arXiv  Detail & Related papers  (2024-01-07T00:58:33Z)
• Distribution-Informed Adaptation for kNN Graph Construction [11.63039933401604]
  We propose the Distribution-Informed adaptive kNN Graph (DaNNG), which combines adaptive kNN with distribution-aware graph construction. DaNNG significantly improves performance on ambiguous samples, and hence enhance overall accuracy and generalization capability.
  arXiv  Detail & Related papers  (2023-08-04T16:14:43Z)
• Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection [4.358468367889626]
  We propose a framework to establish a common evaluation protocol for Graph Neural Networks (GNNs) We motivate and justify it by demonstrating the differences with and without the protocol. We find that by ensuring the same evaluation criteria is followed, there may be significant differences from the reported performance of methods at this task.
  arXiv  Detail & Related papers  (2023-05-10T10:22:28Z)
• GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models [74.43215520371506]
  We present a new framework, called GREAT Score, for global robustness evaluation of adversarial perturbation using generative models. We show high correlation and significantly reduced cost of GREAT Score when compared to the attack-based model ranking on RobustBench. GREAT Score can be used for remote auditing of privacy-sensitive black-box models.
  arXiv  Detail & Related papers  (2023-04-19T14:58:27Z)
• Variational Voxel Pseudo Image Tracking [127.46919555100543]
  Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving. We propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking.
  arXiv  Detail & Related papers  (2023-02-12T13:34:50Z)
• On the Prediction Instability of Graph Neural Networks [2.3605348648054463]
  Instability of trained models can affect reliability, reliability, and trust in machine learning systems. We systematically assess the prediction instability of node classification with state-of-the-art Graph Neural Networks (GNNs) We find that up to one third of the incorrectly classified nodes differ across algorithm runs.
  arXiv  Detail & Related papers  (2022-05-20T10:32:59Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise [51.080620762639434]
  We propose a more efficient parameterization of the posterior approximation for sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks.
  arXiv  Detail & Related papers  (2021-03-15T16:16:18Z)
• A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks [7.139436410105177]
  This research presents a novel genetic algorithm with a hierarchical evaluation strategy (HESGA) The proposed hierarchical strategy uses the fast evaluation in a lower level for recommending candidates to a higher level, where the full evaluation will act as a final assessor to maintain a group of elite individuals.
  arXiv  Detail & Related papers  (2021-01-22T19:19:59Z)
• Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing [88.30867628592112]
  Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. We show that existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. In order to preserve node proximities, we augment the existing GNNs with node representations.
  arXiv  Detail & Related papers  (2020-09-05T16:46:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.