{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:11:00Z","timestamp":1777705860923,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:p>Vertex classification is an important graph mining technique and has important applications in fields such as social recommendation and e-Commerce recommendation. Existing classification methods fail to make full use of the graph topology to improve the classification performance. To alleviate it, we propose a Dual Graph Wavelet neural Network composed of two identical graph wavelet neural networks sharing network parameters. These two networks are integrated with a semi-supervised loss function and carry out supervised learning and unsupervised learning on two matrixes representing the graph topology extracted from the same graph dataset, respectively. One matrix embeds the local consistency information and the other the global consistency information. To reduce the computational complexity of the convolution operation of the graph wavelet neural network, we design an approximate scheme based on the first type Chebyshev polynomial. Experimental results show that the proposed network significantly outperforms the state-of-the-art approaches for vertex classification on all three benchmark datasets and the proposed approximation scheme is validated for datasets with low vertex average degree when the approximation order is small.<\/jats:p>","DOI":"10.3233\/jifs-211729","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T12:02:31Z","timestamp":1635249751000},"page":"5177-5188","source":"Crossref","is-referenced-by-count":1,"title":["Dual graph wavelet neural network for graph-based semi-supervised classification"],"prefix":"10.1177","volume":"42","author":[{"given":"Kekun","family":"Hu","sequence":"first","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Gang","family":"Dong","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Yaqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Rengang","family":"Li","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Dongdong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Yinyin","family":"Chao","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Haiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]},{"given":"Yuan","family":"Ge","sequence":"additional","affiliation":[{"name":"Inspur Electronic Information Industry Co., Ltd., Jinan, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211729_ref3","doi-asserted-by":"crossref","unstructured":"Bhagat S. , Cormode G. and Muthukrishnan S. , Node classification in social networks, Springer, Boston, USA: Social network data analytics, 2011.","DOI":"10.1007\/978-1-4419-8462-3_5"},{"issue":"4","key":"10.3233\/JIFS-211729_ref5","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1177\/1536867X1601600407","article-title":"Support vector machines","volume":"16","author":"Guenther","year":"2016","journal-title":"The Stata Journal"},{"issue":"3","key":"10.3233\/JIFS-211729_ref6","first-page":"1","article-title":"Learning k for kNN classification","volume":"8","author":"Zhang","year":"2017","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"1","key":"10.3233\/JIFS-211729_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"Journal of big Data"},{"key":"10.3233\/JIFS-211729_ref12","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.neucom.2020.06.014","article-title":"Attention mechanism-based CNN for facial expression recognition","volume":"411","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"issue":"2","key":"10.3233\/JIFS-211729_ref21","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s11063-019-10154-1","article-title":"Finite-time Mittag-Leffler stability of fractional-order quaternion-valued memristive neural networks with impulses","volume":"51","author":"Pratap","year":"2020","journal-title":"Neural Processing Letters"},{"issue":"2","key":"10.3233\/JIFS-211729_ref22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on graphs via spectral graph theory","volume":"30","author":"Hammond","year":"2011","journal-title":"Applied and Computational Harmonic Analysis"},{"issue":"1","key":"10.3233\/JIFS-211729_ref26","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/JIFS-211729_ref27","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.neucom.2015.04.042","article-title":", Kernel flexible manifold embedding for pattern classification","volume":"167","author":"El Traboulsi","year":"2015","journal-title":"Neurocomputing"},{"issue":"1","key":"10.3233\/JIFS-211729_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13662-020-02974-6","article-title":"Green-Haar wavelets method for generalized fractional differential equations","volume":"2020","author":"ur Rehman","year":"2020","journal-title":"Advances in Difference Equations"}],"container-title":["Journal of Intelligent &amp; 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