Graph partitioning and graph neural network based hierarchical graph matching for graph similarity computation

Xu, Haifeng; Duan, Ziheng; Wang, Yueyang; Chen, Runjian; Zhang, Qianru; Xu, Zhongbin

doi:10.1016/j.neucom.2021.01.068

Cited by 28 publications

(13 citation statements)

References 37 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of these approaches fit within the framework of "neural message passing" proposed by Gilmer et al [24] . In the message-passing framework, a GNN is viewed as a message-passing algorithm where node representations are iteratively computed from the features of their neighbor nodes using a differentiable aggregation function [25][26][27] .…”

Section: Gnnmentioning

confidence: 99%

Multivariate Time Series Forecasting with Transfer Entropy Graph

Xu¹,

Huang²,

Duan³

et al. 2023

Tsinghua Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help in making decisions. To date, many MTS forecasting methods have been proposed and widely applied. However, these methods assume that the predicted value of a single variable is affected by all other variables, ignoring the causal relationship among variables. To address the above issue, we propose a novel end-to-end deep learning model, termed graph neural network with neural Granger causality, namely CauGNN, in this paper. To characterize the causal information among variables, we introduce the neural Granger causality graph in our model. Each variable is regarded as a graph node, and each edge represents the casual relationship between variables. In addition, convolutional neural network filters with different perception scales are used for time series feature extraction, to generate the feature of each node. Finally, the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the MTS. Three benchmark datasets from the real world are used to evaluate the proposed CauGNN, and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.

show abstract

Section: Gnnmentioning

confidence: 99%

Multivariate Time Series Forecasting with Transfer Entropy Graph

Xu¹,

Huang²,

Duan³

et al. 2023

Tsinghua Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…SimGNN [1] and GraphSim [2] derive the corresponding hidden vector differently by applying the convolution operation to the pairwise node similarity matrix or extracting its histogram features. [41] first partitions graphs and then conducts node-wise comparison among subgraphs. MGMN [20] [41] and hypergraph construction [45].…”

Section: Related Workmentioning

confidence: 99%

“…[41] first partitions graphs and then conducts node-wise comparison among subgraphs. MGMN [20] [41] and hypergraph construction [45]. Although our method avoids the above computational burden, it achieves good performance.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference

Lan¹,

Hong²,

Ma³

et al. 2022

Preprint

View full text Add to dashboard Cite

Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction information between two graphs into one hidden vector and then map it to similarity. To cope with this problem, this study proposes a more interpretable end-to-end paradigm for graph similarity learning, named Similarity Computation via Maximum Common Subgraph Inference (INFMCS). Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS). We implicitly infer MCS to obtain the normalized MCS size, with the supervision information being only the similarity score during training. To capture more global information, we also stack some vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. The entire model is quite simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and regression tasks. Ablation experiments verify the effectiveness of the proposed computation paradigm and other components. Also, visualization and statistics of results reveal the interpretability of INFMCS.

show abstract

“…The Literatures [228][229][230][231][232][233][234][235][236][237][238] Figure 7 The GNN Application Summarizations…”

Section: Knowledge Graphmentioning

confidence: 99%

Graph Neural Networks: Taxonomy, Advances and Trends

Zhou,

Zheng,

Huang

et al. 2020

Preprint

View full text Add to dashboard Cite

Graph neural networks provide a powerful toolkit for embedding real-world graphs into lowdimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.

show abstract

Graph partitioning and graph neural network based hierarchical graph matching for graph similarity computation

Cited by 28 publications

References 37 publications

Multivariate Time Series Forecasting with Transfer Entropy Graph

Multivariate Time Series Forecasting with Transfer Entropy Graph

More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference

Graph Neural Networks: Taxonomy, Advances and Trends

Contact Info

Product

Resources

About