{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:42:57Z","timestamp":1767192177527,"version":"3.48.0"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62506145"],"award-info":[{"award-number":["62506145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Multi-view learning has recently gained considerable attention in graph representation learning as it enables the fusion of complementary information from multiple views to enhance representation quality. However, most existing studies neglect that irrelevant views may introduce noise and negatively affect representation quality. To address the issue, we propose a novel multi-view representation learning framework called a View Filter-driven graph representation fusion network, named ViFi. Following the \u201cless for better\u201d principle, the framework focuses on filtering informative views while discarding irrelevant ones. Specifically, an entropy-based adaptive view filter was designed to dynamically filter the most informative views by evaluating their feature\u2013topology entropy characteristics, aiming to not only reduce irrelevance among views but also enhance their complementarity. In addition, to promote more effective fusion of informative views, we propose an optimized fusion mechanism that leverages the filtered views to identify the optimal integration strategy using a novel information gain function. Through extensive experiments on classification and clustering tasks, ViFi demonstrates clear performance advantages over existing state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/e28010026","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:50:21Z","timestamp":1766710221000},"page":"26","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Less for Better: A View Filter-Driven Graph Representation Fusion Network"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5261-4854","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xibei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121708","DOI":"10.1016\/j.ins.2024.121708","article-title":"Robust graph mutual-assistance convolutional networks for semi-supervised node classification tasks","volume":"694","author":"Guo","year":"2025","journal-title":"Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112632","DOI":"10.1016\/j.patcog.2025.112632","article-title":"VQIT-GNN: A collaborative knowledge transfer for node-level structure imbalance","volume":"172","author":"Guan","year":"2026","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hong, Y., Lin, C., Du, Y., Chen, Z., Tenenbaum, J.B., and Gan, C. (2023, January 17\u201324). 3D concept learning and reasoning from multi-view images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00888"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ma, Z., Luo, M., Guo, H., Zeng, Z., Hao, Y., and Zhao, X. (2024, January 11\u201316). Event-radar: Event-driven multi-view learning for multimodal fake news detection. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand.","DOI":"10.18653\/v1\/2024.acl-long.316"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/TNSRE.2019.2940485","article-title":"Deep multi-view feature learning for EEG-based epileptic seizure detection","volume":"27","author":"Tian","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"128539","DOI":"10.1016\/j.neucom.2024.128539","article-title":"Purity Skeleton Dynamic Hypergraph Neural Network","volume":"610","author":"Wang","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9019","DOI":"10.1109\/TKDE.2022.3220789","article-title":"Dual Feature Interaction-based Graph Convolutional Network","volume":"35","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, G., Li, M., Feng, H., and Zhuang, X. (2025). Deeper insights into deep graph convolutional networks: Stability and generalization. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201314.","DOI":"10.1109\/TPAMI.2025.3616350"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Guo, Q., Liu, K., Guan, W., Yang, X., Sun, Q., and Huang, T. (2025). Hierarchical decoupling from global-to-structural dependency for unsupervised graph representation learning on noisy graphs. Knowl.-Based Syst.","DOI":"10.1016\/j.knosys.2025.115146"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110657","DOI":"10.1016\/j.engappai.2025.110657","article-title":"Feature-topology cascade perturbation for graph neural network","volume":"152","author":"Cong","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Brynte, L., Iglesias, J.P., Olsson, C., and Kahl, F. (2024, January 16\u201322). Learning structure-from-motion with graph attention networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00460"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3564754","article-title":"Dynamic multi-view graph neural networks for citywide traffic inference","volume":"17","author":"Dai","year":"2023","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_13","unstructured":"Wang, Y., Yang, X., Liu, K., Sun, Q., Ding, W., and Qian, Y. (2025). Topology Matters: Achieving Fairness in Graph Neural Networks through Heterophily Propagation. Sci. China Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"122151","DOI":"10.1016\/j.eswa.2023.122151","article-title":"GAF-Net: Graph attention fusion network for multi-view semi-supervised classification","volume":"238","author":"Song","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s11063-024-11479-2","article-title":"Gated fusion adaptive graph neural network for urban road traffic flow prediction","volume":"56","author":"Xiong","year":"2024","journal-title":"Neural Process. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8073","DOI":"10.1007\/s10489-024-05567-y","article-title":"Mhgnn: Multi-view fusion based heterogeneous graph neural network","volume":"54","author":"Li","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3769084","article-title":"A Multimodal Semantic Fusion Network with Cross-Modal Alignment for Multimodal Sentiment Analysis","volume":"21","author":"Zhang","year":"2025","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s10278-007-9044-5","article-title":"Information entropy measure for evaluation of image quality","volume":"21","author":"Tsai","year":"2008","journal-title":"J. Digit. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luo, G., Li, J., Su, J., Peng, H., Yang, C., Sun, L., Yu, P.S., and He, L. (2021, January 19\u201327). Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/381"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: Recent progress and new challenges","volume":"38","author":"Zhao","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_21","first-page":"1","article-title":"Self-paced multi-view co-training","volume":"21","author":"Ma","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TKDE.2025.3543377","article-title":"Cross-Graph Interaction Networks","volume":"37","author":"Guo","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_23","unstructured":"Hwang, H., Kim, G.H., Hong, S., and Kim, K.E. (2021, January 6\u201314). Multi-view representation learning via total correlation objective. Proceedings of the Advances in Neural Information Processing Systems, Online."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.procs.2019.12.004","article-title":"Multi-view SVM classification with feature selection","volume":"162","author":"Niu","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"117787","DOI":"10.1016\/j.eswa.2022.117787","article-title":"Multi-view learning with privileged weighted twin support vector machine","volume":"206","author":"Xu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qiao, S., Shen, W., Zhang, Z., Wang, B., and Yuille, A. (2018, January 8\u201314). Deep co-training for semi-supervised image recognition. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"ref_27","unstructured":"Wang, W., Arora, R., Livescu, K., and Bilmes, J. (2015, January 7\u20139). On deep multi-view representation learning. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_28","unstructured":"Mao, L., and Sun, S. (2016, January 9\u201315). Soft margin consistency based scalable multi-view maximum entropy discrimination. Proceedings of the International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, Z., Dong, Z., Yu, C., Yang, K., Fan, Z., and Chen, C.P. (2025). A review on multi-view learning. Front. Comput. Sci., 19.","DOI":"10.1007\/s11704-024-40004-w"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110215","DOI":"10.1016\/j.patcog.2023.110215","article-title":"Bimodal SegNet: Fused instance segmentation using events and RGB frames","volume":"149","author":"Kachole","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wei, X., Yu, R., and Sun, J. (2020, January 13\u201319). View-GCN: View-based graph convolutional network for 3D shape analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00192"},{"key":"ref_32","unstructured":"Liang, T., Xie, H., Yu, K., Xia, Z., Lin, Z., Wang, Y., Tang, T., Wang, B., and Tang, Z. (December, January 28). BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework. Proceedings of the Advances in Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_33","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 8\u201313). Two-Stream Convolutional Networks for Action Recognition in Videos. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"bbad427","DOI":"10.1093\/bib\/bbad427","article-title":"Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases","volume":"25","author":"Su","year":"2023","journal-title":"Briefings Bioinform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19370","DOI":"10.1038\/s41598-023-46660-5","article-title":"Research on the construction of weaponry indicator system and intelligent evaluation methods","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111020","DOI":"10.1016\/j.patcog.2024.111020","article-title":"Collaborative graph neural networks for augmented graphs: A local-to-global perspective","volume":"158","author":"Guo","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_37","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_38","unstructured":"Kipf, T.N., and Welling, M. (2016). Variational Graph Auto-Encoders. arXiv."},{"key":"ref_39","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103708","DOI":"10.1016\/j.artint.2022.103708","article-title":"Multi-view graph convolutional networks with attention mechanism","volume":"307","author":"Yao","year":"2022","journal-title":"Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11432-020-3318-5","article-title":"Deepwalk-aware graph convolutional networks","volume":"65","author":"Jin","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107616","DOI":"10.1016\/j.engappai.2023.107616","article-title":"Perturbation-augmented Graph Convolutional Networks: A Graph Contrastive Learning architecture for effective node classification tasks","volume":"129","author":"Guo","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","unstructured":"Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Ver Steeg, G., and Galstyan, A. (2019, January 9\u201315). MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_44","unstructured":"Abu-El-Haija, S., Kapoor, A., Perozzi, B., and Lee, J. (2019, January 22\u201325). N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. Proceedings of the Conference on Uncertainty in Artificial Intelligence, Tel Aviv, Israel."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ins.2021.05.057","article-title":"Semi-supervised learning with mixed-order graph convolutional networks","volume":"573","author":"Wang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102893","DOI":"10.1016\/j.inffus.2024.102893","article-title":"StrucGCN: Structural enhanced graph convolutional networks for graph embedding","volume":"117","author":"Zhang","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"125274","DOI":"10.1016\/j.eswa.2024.125274","article-title":"Beyond homophily: Neighborhood distribution-guided graph convolutional networks","volume":"259","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_48","unstructured":"Li, S., Li, W.T., and Wang, W. (2020, January 7\u201312). Co-GCN for multi-view semi-supervised learning. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.inffus.2023.02.013","article-title":"Learnable graph convolutional network and feature fusion for multi-view learning","volume":"95","author":"Chen","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5549","DOI":"10.1007\/s13042-024-02260-x","article-title":"Sequential attention layer-wise fusion network for multi-view classification","volume":"15","author":"Teng","year":"2024","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106438","DOI":"10.1016\/j.neunet.2024.106438","article-title":"Heterogeneous graph convolutional network for multi-view semi-supervised classification","volume":"178","author":"Wang","year":"2024","journal-title":"Neural Netw."},{"key":"ref_52","first-page":"1","article-title":"Multi-view graph convolutional networks with differentiable node selection","volume":"18","author":"Chen","year":"2023","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shen, X., Sun, D., Pan, S., Zhou, X., and Yang, L.T. (2023, January 7\u201314). Neighbor Contrastive Learning on Learnable Graph Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA.","DOI":"10.1609\/aaai.v37i8.26168"},{"key":"ref_54","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y. (2020, January 6\u201312). Graph Contrastive Learning with Augmentations. Proceedings of the Advances in Neural Information Processing Systems, Virtually."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.neunet.2023.04.001","article-title":"Graph contrastive learning with implicit augmentations","volume":"163","author":"Liang","year":"2023","journal-title":"Neural Netw."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L. (2021, January 19\u201323). Graph Contrastive Learning with Adaptive Augmentation. Proceedings of the ACM Web Conference, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3449802"},{"key":"ref_57","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2022, January 14\u201318). DeepWalk: Online learning of social representations. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA."},{"key":"ref_58","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_59","first-page":"583","article-title":"Cluster ensembles\u2014A knowledge reuse framework for combining multiple partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Classif."},{"key":"ref_61","unstructured":"Liu, S., Ying, R., Dong, H., Li, L., Xu, T., Rong, Y., Zhao, P., Huang, J., and Wu, D. (2022, January 17\u201323). Local Augmentation for Graph Neural Networks. Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhu, M., Bo, D., Cui, P., Shi, C., and Pei, J. (2020, January 6\u201310). AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403177"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/1\/26\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:39:27Z","timestamp":1767191967000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/1\/26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,24]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["e28010026"],"URL":"https:\/\/doi.org\/10.3390\/e28010026","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2025,12,24]]}}}