PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes

• URL: http://arxiv.org/abs/2208.03075v1
• Date: Fri, 5 Aug 2022 10:14:48 GMT
• Title: PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes
• Authors: Tien-Cuong Bui, Wen-syan Li, Sang-Kyun Cha
• Abstract summary: We propose a multi-level GNN explanation framework based on an observation that GNN is a multimodal learning process of multiple components in graph data. The complexity of the original problem is relaxed by breaking into multiple sub-parts represented as a hierarchical structure. We also aim for personalized explanations as the framework can generate different results based on user preferences.
• Score: 0.2005299372367689
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph Neural Networks (GNNs) are widely adopted in advanced AI systems due to their capability of representation learning on graph data. Even though GNN explanation is crucial to increase user trust in the systems, it is challenging due to the complexity of GNN execution. Lately, many works have been proposed to address some of the issues in GNN explanation. However, they lack generalization capability or suffer from computational burden when the size of graphs is enormous. To address these challenges, we propose a multi-level GNN explanation framework based on an observation that GNN is a multimodal learning process of multiple components in graph data. The complexity of the original problem is relaxed by breaking into multiple sub-parts represented as a hierarchical structure. The top-level explanation aims at specifying the contribution of each component to the model execution and predictions, while fine-grained levels focus on feature attribution and graph structure attribution analysis based on knowledge distillation. Student models are trained in standalone modes and are responsible for capturing different teacher behaviors, later used for particular component interpretation. Besides, we also aim for personalized explanations as the framework can generate different results based on user preferences. Finally, extensive experiments demonstrate the effectiveness and fidelity of our proposed approach.

Related papers

• GNNAnatomy: Systematic Generation and Evaluation of Multi-Level Explanations for Graph Neural Networks [20.05098366613674]
  We introduce GNNAnatomy, a visual analytics system designed to generate and evaluate multi-level explanations for graph classification tasks. GNNAnatomy uses graphlets, primitive graph substructures, to identify the most critical substructures in a graph class by analyzing the correlation between GNN predictions and graphlet frequencies. We demonstrate the effectiveness of GNNAnatomy through case studies on synthetic and real-world graph datasets from sociology and biology domains.
  arXiv  Detail & Related papers  (2024-06-06T23:09:54Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling [60.0185734837814]
  Graph neural networks (GNNs) have found extensive applications in learning from graph data. To bolster the generalization capacity of GNNs, it has become customary to augment training graph structures with techniques like graph augmentations. This study introduces the concept of Mixture-of-Experts (MoE) to GNNs, with the aim of augmenting their capacity to adapt to a diverse range of training graph structures.
  arXiv  Detail & Related papers  (2023-04-06T01:09:36Z)
• Structural Explanations for Graph Neural Networks using HSIC [21.929646888419914]
  Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. The complicated dynamics of GNNs make it difficult to understand which parts of the graph features contribute more strongly to the predictions. In this study, a flexible model agnostic explanation method is proposed to detect significant structures in graphs.
  arXiv  Detail & Related papers  (2023-02-04T09:46:47Z)
• Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation [0.17842332554022688]
  Graph Neural Networks (GNNs) have become increasingly ubiquitous in numerous applications and systems, necessitating explanations of their predictions. We propose a novel GNN explanation framework named SCALE, which is general and fast for explaining predictions. In training, a black-box GNN model guides learners based on an online knowledge distillation paradigm. Specifically, edge masking and random walk with restart procedures are executed to provide structural explanations for graph-level and node-level predictions.
  arXiv  Detail & Related papers  (2022-10-20T08:44:57Z)
• Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks [0.27195102129094995]
  We use a random graph generator to investigate how the graph size and structural properties affect the predictive performance of GNNs. We present specific evidence that the average node degree is a key feature in determining whether GNNs can generalize to unseen graphs. We propose a multi- module GNN framework that allows the network to adapt flexibly to new graphs by generalizing a single canonical nonlinear transformation over aggregated inputs.
  arXiv  Detail & Related papers  (2022-09-14T12:13:59Z)
• Task-Agnostic Graph Explanations [50.17442349253348]
  Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data. Existing learning-based GNN explanation approaches are task-specific in training. We propose a Task-Agnostic GNN Explainer (TAGE) trained under self-supervision with no knowledge of downstream tasks.
  arXiv  Detail & Related papers  (2022-02-16T21:11:47Z)
• Parameterized Explainer for Graph Neural Network [49.79917262156429]
  We propose PGExplainer, a parameterized explainer for Graph Neural Networks (GNNs) Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification.
  arXiv  Detail & Related papers  (2020-11-09T17:15:03Z)
• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting [63.04999833264299]
  "Graph Substructure Networks" (GSN) is a topologically-aware message passing scheme based on substructure encoding. We show that it is strictly more expressive than the Weisfeiler-Leman (WL) graph isomorphism test. We perform an extensive evaluation on graph classification and regression tasks and obtain state-of-the-art results in diverse real-world settings.
  arXiv  Detail & Related papers  (2020-06-16T15:30:31Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)

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.