Structural Explanations for Graph Neural Networks using HSIC
- URL: http://arxiv.org/abs/2302.02139v1
- Date: Sat, 4 Feb 2023 09:46:47 GMT
- Title: Structural Explanations for Graph Neural Networks using HSIC
- Authors: Ayato Toyokuni, Makoto Yamada
- Abstract summary: 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.
- Score: 21.929646888419914
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph neural networks (GNNs) are a type of neural model that tackle graphical
tasks in an end-to-end manner. Recently, GNNs have been receiving increased
attention in machine learning and data mining communities because of the higher
performance they achieve in various tasks, including graph classification, link
prediction, and recommendation. However, the complicated dynamics of GNNs make
it difficult to understand which parts of the graph features contribute more
strongly to the predictions. To handle the interpretability issues, recently,
various GNN explanation methods have been proposed. In this study, a flexible
model agnostic explanation method is proposed to detect significant structures
in graphs using the Hilbert-Schmidt independence criterion (HSIC), which
captures the nonlinear dependency between two variables through kernels. More
specifically, we extend the GraphLIME method for node explanation with a group
lasso and a fused lasso-based node explanation method. The group and fused
regularization with GraphLIME enables the interpretation of GNNs in
substructure units. Then, we show that the proposed approach can be used for
the explanation of sequential graph classification tasks. Through experiments,
it is demonstrated that our method can identify crucial structures in a target
graph in various settings.
Related papers
- GNNAnatomy: Systematic Generation and Evaluation of Multi-Level Explanations for Graph Neural Networks [20.05098366613674]
Graph Neural Networks (GNNs) have proven highly effective in various machine learning (ML) tasks involving graphs, such as node/graph classification and link prediction.
Explaining the decisions made by GNNs poses challenges because of the aggregated relational information based on graph structure, leading to complex data transformations.
We introduce GNNAnatomy, a model- and dataset-agnostic visual analytics system designed to facilitate the generation and evaluation of multi-level explanations for GNNs.
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) - Deep Graph Neural Networks via Flexible Subgraph Aggregation [50.034313206471694]
Graph neural networks (GNNs) can learn from graph-structured data and learn the representation of nodes through aggregating neighborhood information.
In this paper, we evaluate the expressive power of GNNs from the perspective of subgraph aggregation.
We propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation.
arXiv Detail & Related papers (2023-05-09T12:03:42Z) - Relation Embedding based Graph Neural Networks for Handling
Heterogeneous Graph [58.99478502486377]
We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs.
Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
arXiv Detail & Related papers (2022-09-23T05:24:18Z) - Explainability in subgraphs-enhanced Graph Neural Networks [12.526174412246107]
Subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of GNNs.
In this work, we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs.
We show that our framework is successful in explaining the decision process of an SGNN on graph classification tasks.
arXiv Detail & Related papers (2022-09-16T13:39:10Z) - Discovering the Representation Bottleneck of Graph Neural Networks from
Multi-order Interactions [51.597480162777074]
Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions.
Recent works point out that different graph learning tasks require different ranges of interactions between nodes.
We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
arXiv Detail & Related papers (2022-05-15T11:38:14Z) - SEEN: Sharpening Explanations for Graph Neural Networks using
Explanations from Neighborhoods [0.0]
We propose a method to improve the explanation quality of node classification tasks through aggregation of auxiliary explanations.
Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques.
Experiments on matching motif-participating nodes from a given graph show great improvement in explanation accuracy of up to 12.71%.
arXiv Detail & Related papers (2021-06-16T03:04:46Z) - A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data.
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem.
We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
arXiv Detail & Related papers (2020-10-05T04:57:18Z) - 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) - GraphLIME: Local Interpretable Model Explanations for Graph Neural
Networks [45.824642013383944]
Graph neural networks (GNN) were shown to be successful in effectively representing graph structured data.
We propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso.
arXiv Detail & Related papers (2020-01-17T09:50:28Z)
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.