Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion
Over the past few years, there has been a surge in research attention towards tasks involving graph data, largely due to the impressive performance demonstrated by graph neural networks (GNNs) in handling such information. Currently, out-of-distribution (OOD) detection in graphs is a hot research to...
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MDPI AG
2024-09-01
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| Series: | Mathematics |
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| author | Fangfang Li Yangshuai Wang Xinyu Du Xiaohua Li Ge Yu |
| author_facet | Fangfang Li Yangshuai Wang Xinyu Du Xiaohua Li Ge Yu |
| author_sort | Fangfang Li |
| collection | DOAJ |
| description | Over the past few years, there has been a surge in research attention towards tasks involving graph data, largely due to the impressive performance demonstrated by graph neural networks (GNNs) in handling such information. Currently, out-of-distribution (OOD) detection in graphs is a hot research topic. The goal of graph OOD detection is to identify nodes or new graphs that differ from the training data distribution, primarily in terms of attributes and structures. OOD detection is crucial for enhancing the stability, security, and robustness of models. In various applications, such as biological networks and financial fraud, graph OOD detection can help models identify anomalies or unforeseen situations, thereby enabling appropriate responses. In node-level OOD detection, existing models typically only consider first-order neighbors. This paper introduces graph diffusion to the OOD detection task for the first time, proposing the HOOD model, a graph diffusion-based OOD node detection algorithm. Specifically, the original graph is processed through graph diffusion to obtain a new graph that can directly capture high-order neighbor information, overcoming the limitation that message passing must go through first-order neighbors. The new graph is then sparsified using a top-k approach. Based on entropy information, regularization is employed to ensure the uncertainty of OOD nodes, thereby giving these nodes higher scores and enabling the model to effectively detect OOD nodes while ensuring the accuracy of in-distribution node classification. Experimental results demonstrate that the HOOD model outperforms existing methods in both node classification and OOD detection tasks on multiple benchmarks, highlighting its robustness and effectiveness. |
| format | Article |
| id | doaj-art-cab56f4fe97141d8bd7734476dbeae92 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-cab56f4fe97141d8bd7734476dbeae922025-08-20T01:55:38ZengMDPI AGMathematics2227-73902024-09-011218294210.3390/math12182942Out-of-Distribution Node Detection Based on Graph Heat Kernel DiffusionFangfang Li0Yangshuai Wang1Xinyu Du2Xiaohua Li3Ge Yu4School of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaOver the past few years, there has been a surge in research attention towards tasks involving graph data, largely due to the impressive performance demonstrated by graph neural networks (GNNs) in handling such information. Currently, out-of-distribution (OOD) detection in graphs is a hot research topic. The goal of graph OOD detection is to identify nodes or new graphs that differ from the training data distribution, primarily in terms of attributes and structures. OOD detection is crucial for enhancing the stability, security, and robustness of models. In various applications, such as biological networks and financial fraud, graph OOD detection can help models identify anomalies or unforeseen situations, thereby enabling appropriate responses. In node-level OOD detection, existing models typically only consider first-order neighbors. This paper introduces graph diffusion to the OOD detection task for the first time, proposing the HOOD model, a graph diffusion-based OOD node detection algorithm. Specifically, the original graph is processed through graph diffusion to obtain a new graph that can directly capture high-order neighbor information, overcoming the limitation that message passing must go through first-order neighbors. The new graph is then sparsified using a top-k approach. Based on entropy information, regularization is employed to ensure the uncertainty of OOD nodes, thereby giving these nodes higher scores and enabling the model to effectively detect OOD nodes while ensuring the accuracy of in-distribution node classification. Experimental results demonstrate that the HOOD model outperforms existing methods in both node classification and OOD detection tasks on multiple benchmarks, highlighting its robustness and effectiveness.https://www.mdpi.com/2227-7390/12/18/2942out-of-distributiongraph diffusionregularizationgraph neural network |
| spellingShingle | Fangfang Li Yangshuai Wang Xinyu Du Xiaohua Li Ge Yu Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion Mathematics out-of-distribution graph diffusion regularization graph neural network |
| title | Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion |
| title_full | Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion |
| title_fullStr | Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion |
| title_full_unstemmed | Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion |
| title_short | Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion |
| title_sort | out of distribution node detection based on graph heat kernel diffusion |
| topic | out-of-distribution graph diffusion regularization graph neural network |
| url | https://www.mdpi.com/2227-7390/12/18/2942 |
| work_keys_str_mv | AT fangfangli outofdistributionnodedetectionbasedongraphheatkerneldiffusion AT yangshuaiwang outofdistributionnodedetectionbasedongraphheatkerneldiffusion AT xinyudu outofdistributionnodedetectionbasedongraphheatkerneldiffusion AT xiaohuali outofdistributionnodedetectionbasedongraphheatkerneldiffusion AT geyu outofdistributionnodedetectionbasedongraphheatkerneldiffusion |