[1] YE X B, GUAN Q, LUO W, et al. Molecular substructure graph attention network for molecular property identification in drug discovery[J]. Pattern Recognition, 2022, 128: 108659.
[2] PENG H, LI J, HE Y, et al. Large-scale hierarchical text classification with recursively regularized deep graph-CNN[C]//Proceedings of the 2018 World Wide Web Conference on World Wide Web(WWW’18), Lyon, France, 2018: 1063-1072.
[3] WANG D, QI Y, LIN J, et al. A semi-supervised graph attentive network for financial fraud detection[C]//Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China, 2019: 598-607.
[4] YING R, HE R, CHEN K, et al. Graph convolutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, United Kingdom, 2018: 974-983.
[5] LIU Y, SUN Z, ZHANG W. Improving fraud detection via hierarchical attention-based graph neural network[J]. Journal of Information Security and Applications, 2023, 72: 103399.
[6] ZHANG J, HE X, QING L, et al. Multi-relation graph convolutional network for Alzheimer’s disease diagnosis using structural MRI[J]. Knowledge-Based Systems, 2023, 270: 110546.
[7] GORI M, MONFARDINI G, SCARSELLI F. A new model for learning in graph domains[C]//Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, 2005: 729-734.
[8] LUO X, ZHAO Y, QIN Y, et al. Towards semi-supervised universal graph classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2023: 1-13.
[9] WANG Z, WANG Q, ZHU T, et al. Extending LINE for network embedding with completely imbalanced labels[J]. International Journal of Data Warehousing and Mining, 2020, 16(3): 20-36.
[10] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357.
[11] YUN S, KIM K, YOON K, et al. LTE4G: long-tail experts for graph neural networks[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022: 2434-2443.
[12] ZHAO T, ZHANG X, WANG S. GraphSMOTE: imbalanced node classification on graphs with graph neural networks[C]//Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021: 833-841.
[13] QU L, ZHU H, ZHENG R, et al. ImGAGN: imbalanced network embedding via generative adversarial graph networks[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021: 1390-1398.
[14] PARK J, SONG J G, YANG E. GraphENS: neighbor-aware ego network synthesis for class-imbalanced node classification[C]//Proceedings of the International Conference on Learning Representations, 2022.
[15] ANDERSEN R, CHUNG F, LANG K. Local graph partitioning using PageRank vectors[C]//Proceedings of the 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06), Berkeley, CA, USA, 2006: 475-486.
[16] SEN P, NAMATA G, BILGIC M, et al. Collective classification in network data[J]. AI Magazine, 2008, 29(3): 93-106.
[17] BOJCHEVSKI A, GüNNEMANN S. Deep Gaussian embedding of graphs: unsupervised inductive learning via ranking[J]. arXiv:1707.03815, 2017.
[18] YUAN B, MA X L. Sampling + reweighting: boosting the performance of AdaBoost on imbalanced datasets[C]//Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, 2012: 1-6.
[19] ANDO S, HUANG C Y. Deep over-sampling framework for classifying imbalanced data[M]//CECI M, HOLLMéN J, TODOROVSKI L, et al. Machine learning and knowledge discovery in databases. Cham: Springer International Publishing, 2017: 770-785.
[20] KINGMA D P, BA J. Adam: a method for stochastic optimization[J]. arXiv:1412.6980, 2014. |