{"type":"ScholarlyArticle","title":"KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification","authors":[{"name":"Likang Wu","slug":"likang-wu","given":"Likang","family":"Wu","affiliation":"University of Science and Technology of China ,; State Key Laboratory of Cognitive Intelligence , China ,"},{"name":"Junji Jiang","slug":"junji-jiang","given":"Junji","family":"Jiang","affiliation":"Fudan University ,"},{"name":"Hongke Zhao","slug":"hongke-zhao","given":"Hongke","family":"Zhao","affiliation":"Tianjin University ,"},{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"University of Science and Technology of China ,; State Key Laboratory of Cognitive Intelligence , China ,"},{"name":"Defu Lian","slug":"defu-lian","given":"Defu","family":"Lian","affiliation":"University of Science and Technology of China ,; State Key Laboratory of Cognitive Intelligence , China ,"},{"name":"Mengdi Zhang","slug":"mengdi-zhang","given":"Mengdi","family":"Zhang","affiliation":"Meituan-Dianping Group"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"University of Science and Technology of China ,; State Key Laboratory of Cognitive Intelligence , China ,"}],"abstract":"Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes and labels' semantics thus enabling knowledge transfer from seen to unseen classes. However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i.e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels. It's necessary to separate and judge the semantic factors that tremendously affect the cognitive ability to improve the generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics via the extracted KG (Knowledge Graph)-based topics. And then the content of each node is reconstructed to a topic-level representation that offers multi-faceted and fine-grained semantic relevancy to different labels. Due to the particularity of the graph's instance (i.e., node) representation, a novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation. Finally, we conduct extensive experiments on several public graph datasets and design an application of zero-shot cross-domain recommendation. The quantitative results demonstrate both the effectiveness and generalization of KMF with the comparison of state-of-the-art baselines.","doi":"10.24963/ijcai.2023/262","url":"https://ijcai.org/proceedings/2023/262","pdf":"https://ijcai.org/proceedings/2023/0262.pdf","markdown":"https://ijcai.org/proceedings/2023/262.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/262","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":262,"pages":{"first":2361,"last":2369},"keywords":["Data Mining: DM: Knowledge graphs and knowledge base completion","Data Mining: DM: Applications","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Bergstra et al., 2013] James Bergstra, Dan Yamins, and David D Cox. Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. In the 12th Python in science conference, pages 13-20, 2013.","title":"Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms","authors":"James Bergstra, Dan Yamins, David D Cox","year":"2013","venue":"the 12th Python in science conference","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Bojchevski and Günnemann, ] Aleksandar Bojchevski and Stephan Günnemann. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In 6th ICLR 2018, Vancouver, BC, Canada, April 30 -May 3.","title":"Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking","authors":"Günnemann Bojchevski, Aleksandar Bojchevski, Stephan Günnemann","year":null,"venue":"6th ICLR 2018","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bronstein et al., 2017] Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18-42, 2017.","title":"Geometric deep learning: going beyond euclidean data","authors":"Joan Michael M Bronstein, Yann Bruna, Arthur Lecun, Pierre Szlam, Vandergheynst","year":"2017","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2020] Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. Simple and deep graph convolutional networks. In International Confer- ence on Machine Learning, pages 1725-1735, 2020.","title":"Simple and deep graph convolutional networks","authors":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chiang et al., 2019] Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In ACM SIGKDD, pages 257- 266, 2019.","title":"Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks","authors":"Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh","year":"2019","venue":"ACM SIGKDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pages 4171-4186, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Farhadi et al., 2009] Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth. Describing objects by their attributes. computer vision and pattern recognition, 2009.","title":"Describing objects by their attributes. computer vision and pattern recognition","authors":"Ali Farhadi, Ian Endres, Derek Hoiem, David Forsyth","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Frome et al., 2013] Andrea Frome, Greg Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ran- zato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. NeurIPS, 2013.","title":"Devise: A deep visual-semantic embedding model","authors":"Andrea Frome, Greg Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'aurelio Ranzato, Tomas Mikolov","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gao et al., 2018] Yuqing Gao, Jisheng Liang, Benjamin Han, Mohamed Yakout, and Ahmed Mohamed. Building a large-scale, accurate and fresh knowledge graph. KDD, Tutorial 39, 2018.","title":"Building a large-scale, accurate and fresh knowledge graph","authors":"Yuqing Gao, Jisheng Liang, Benjamin Han, Mohamed Yakout, Ahmed Mohamed","year":"2018","venue":"KDD, Tutorial","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Geng et al., 2021] Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, and Huajun Chen. Ontozsl: Ontology-enhanced zero-shot learning. In Proceedings of the Web Conference 2021, pages 3325-3336, 2021.","title":"Ontozsl: Ontology-enhanced zero-shot learning","authors":"Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen","year":"2021","venue":"Proceedings of the Web Conference 2021","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hamilton et al., 2017] William L. Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Process- ing Systems, December 4-9, 2017, Long Beach, CA, USA, pages 1024-1034, 2017.","title":"Inductive representation learning on large graphs","authors":"William L Hamilton, Zhitao Ying, Jure Leskovec","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Han et al., 2023] Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, and Enhong Chen. Guesr: A global unsupervised data- enhancement with bucket-cluster sampling for sequential recommendation. In DASFAA 2023, Tianjin, China, April 17-20, 2023, pages 286-296. Springer, 2023.","title":"Guesr: A global unsupervised dataenhancement with bucket-cluster sampling for sequential recommendation","authors":"Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, Enhong Chen","year":"2023","venue":"DASFAA 2023","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Harrando and Troncy, 2021] Ismail Harrando and Raphaël Troncy. Explainable zero-shot topic extraction using a common-sense knowledge graph. language data and knowledge, 2021.","title":"Explainable zero-shot topic extraction using a common-sense knowledge graph","authors":"Troncy Harrando, Ismail Harrando, Raphaël Troncy","year":"2021","venue":"language data and knowledge","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Henaff et al., 2015] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015.","title":"Deep convolutional networks on graph-structured data","authors":"Mikael Henaff, Joan Bruna, Yann Lecun","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hinton et al., 2015] Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. Distilling the knowledge in a neural net- work. CoRR, abs/1503.02531, 2015.","title":"Distilling the knowledge in a neural network","authors":"Geoffrey E Hinton, Oriol Vinyals, Jeffrey Dean","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kampffmeyer et al., 2019] Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, and Eric P. Xing. Cvpr. pages 11487-11496. Computer Vision Foun- dation / IEEE, 2019.","title":"Cvpr","authors":"Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P Xing","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kingma and Ba, 2015] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, 3rd International Con- ference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":"3rd International Conference on Learning Representations, ICLR 2015","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kipf and Welling, 2017] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"Thomas N Kipf, Max Welling","year":"2017","venue":"5th International Conference on Learning Representations, ICLR 2017","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lampert et al., 2009] Christoph H. Lampert, Hannes Nick- isch, and Stefan Harmeling. Learning to detect unseen ob- ject classes by between-class attribute transfer. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, pages 951-958, 2009.","title":"Learning to detect unseen object classes by between-class attribute transfer","authors":"Christoph H Lampert, Hannes Nickisch, Stefan Harmeling","year":"2009","venue":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lampert et al., 2013] Christoph H Lampert, Hannes Nick- isch, and Stefan Harmeling. Attribute-based classifica- tion for zero-shot visual object categorization. IEEE transactions on pattern analysis and machine intelligence, 36(3):453-465, 2013.","title":"Attribute-based classification for zero-shot visual object categorization","authors":"Hannes Christoph H Lampert, Stefan Nickisch, Harmeling","year":"2013","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2020] Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, and Tao Mei. Learning the compositional visual coherence for complementary recommendations. arXiv preprint arXiv:2006.04380, 2020.","title":"Learning the compositional visual coherence for complementary recommendations","authors":"Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei","year":"2020","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2020/489"},{"position":22,"raw":"[Liu et al., 2020a] Huan Liu, Lina Yao, Qinghua Zheng, Minnan Luo, Hongke Zhao, and Yanzhang Lyu. Dual- stream generative adversarial networks for distributionally robust zero-shot learning. Information Sciences, 519:407- 422, 2020.","title":"Dualstream generative adversarial networks for distributionally robust zero-shot learning","authors":"Liu","year":"2020","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2020b] Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, and Yu-Gang Jiang. Hyperbolic visual embedding learning for zero-shot recognition. In CVPR, pages 9270-9278. Computer Vision Foundation / IEEE, 2020.","title":"Hyperbolic visual embedding learning for zero-shot recognition","authors":"Liu","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2021] Danyang Liu, Jianxun Lian, Zheng Liu, Xiting Wang, Guangzhong Sun, and Xing Xie. Reinforced anchor knowledge graph generation for news recommen- dation reasoning. In ACM SIGKDD '21:, 2021.","title":"Reinforced anchor knowledge graph generation for news recommendation reasoning","authors":"Danyang Liu, Jianxun Lian, Zheng Liu, Xiting Wang, Guangzhong Sun, Xing Xie","year":"2021","venue":"ACM SIGKDD '21","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(Nov):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton","authors":"Hinton Maaten","year":"2008","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Norouzi et al., 2014] Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean. Zero-shot learning by convex combination of semantic embeddings. ICLR, 2014.","title":"Zero-shot learning by convex combination of semantic embeddings","authors":"Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, Jeffrey Dean","year":"2014","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Pan et al., 2016] Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. Tri-party deep network representation. Network, 11(9):12, 2016.","title":"Tri-party deep network representation","authors":"Pan","year":"2016","venue":"Network","doi":null,"url":null,"internal_url":"/proceedings/2016/271"},{"position":28,"raw":"[Romera-Paredes and Torr, 2015] Bernardino Romera- Paredes and Philip Torr. An embarrassingly simple approach to zero-shot learning. In International confer- ence on machine learning, pages 2152-2161, 2015.","title":"Bernardino Romera-Paredes and Philip Torr. An embarrassingly simple approach to zero-shot learning","authors":"Romera-Paredes, Torr","year":"2015","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Scarselli et al., 2008] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE Transactions on Neural Networks, 20(1):61-80, 2008.","title":"The graph neural network model","authors":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini","year":"2008","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"Speer et al., ] Robyn Speer, Joshua Chin, and Catherine Havasi. Conceptnet 5.5: An open multilingual graph of general knowledge. In AAAI, 2017, San Francisco, Cali- fornia, USA, pages 4444-4451.","title":"Conceptnet 5.5: An open multilingual graph of general knowledge","authors":"Speer","year":null,"venue":"AAAI, 2017","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"Velickovic et al., ] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In 6th Inter- national Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 -May 3, 2018.","title":"Graph attention networks","authors":"Velickovic","year":"2018","venue":"6th International Conference on Learning Representations, ICLR 2018","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wan et al., 2019] Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, and Jing Liao. Transductive zero-shot learning with visual structure con- straint. NurIPS, 32, 2019.","title":"Transductive zero-shot learning with visual structure constraint","authors":"Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao","year":"2019","venue":"NurIPS","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2018a] Xiaolong Wang, Yufei Ye, and Abhinav Gupta. Zero-shot recognition via semantic embeddings and knowledge graphs. In CVPR, pages 6857-6866. Com- puter Vision Foundation / IEEE Computer Society, 2018.","title":"Zero-shot recognition via semantic embeddings and knowledge graphs","authors":"Wang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2018b] Zheng Wang, Xiaojun Ye, Chaokun Wang, Yuexin Wu, Changping Wang, and Kaiwen Liang. Rsdne: Exploring relaxed similarity and dissimilarity from completely-imbalanced labels for network embedding. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 32, 2018.","title":"Rsdne: Exploring relaxed similarity and dissimilarity from completely-imbalanced labels for network embedding","authors":"Wang","year":"2018","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2019a] Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su. Mcne: An end-to-end framework for learning multiple conditional network representations of social network. In ACM SIGKDD' 19, pages 1064-1072, 2019.","title":"Mcne: An end-to-end framework for learning multiple conditional network representations of social network","authors":"Wang","year":"2019","venue":"ACM SIGKDD' 19","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2019b] Wei Wang, Vincent W. Zheng, Han Yu, and Chunyan Miao. A survey of zero-shot learning: Set- tings, methods, and applications. ACM Transactions on Intelligent Systems and Technology, 2019.","title":"A survey of zero-shot learning: Settings, methods, and applications","authors":"Wang","year":"2019","venue":"ACM Transactions on Intelligent Systems and Technology","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2021] Zheng Wang, Jialong Wang, Yuchen Guo, and Zhiguo Gong. Zero-shot node classification with decomposed graph prototype network. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Dis- covery & Data Mining, pages 1769-1779, 2021.","title":"Zero-shot node classification with decomposed graph prototype network","authors":"Zheng Wang, Jialong Wang, Yuchen Guo, Zhiguo Gong","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2020] Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, and Chunyan Miao. Multi-label causal feature selection. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6430-6437, 2020.","title":"Multi-label causal feature selection","authors":"Xingyu Wu, Bingbing Jiang, Kui Yu, Huanhuan Chen, Chunyan Miao","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wu et al., 2021] Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Jun Wang, Mengdi Zhang, and Enhong Chen. Learning the implicit semantic representation on graph-structured data. arXiv preprint arXiv:2101.06471, 2021.","title":"Learning the implicit semantic representation on graph-structured data","authors":"Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Jun Wang, Mengdi Zhang, Enhong Chen","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wu et al., 2023a] Likang Wu, Hongke Zhao, Zhi Li, Zhenya Huang, Qi Liu, and Enhong Chen. Learning the ex- plainable semantic relations via unified graph topic- disentangled neural networks. ACM Transactions on Knowledge Discovery from Data, 2023.","title":"Learning the explainable semantic relations via unified graph topicdisentangled neural networks","authors":"Wu","year":"2023","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wu et al., 2023b] Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al. A survey on large language models for recommendation. arXiv preprint arXiv:2305.19860, 2023.","title":"A survey on large language models for recommendation","authors":"Wu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yin et al., 2019] Wenpeng Yin, Jamaal Hay, and Dan Roth. Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach. empirical methods in natural language processing, 2019.","title":"Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach. empirical methods in natural language processing","authors":"Yin","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yu et al., 2022] Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Sanshi Lei Yu, and Zaixi Zhang. Untargeted at- tack against federated recommendation systems via poi- sonous item embeddings and the defense. arXiv preprint arXiv:2212.05399, 2022.","title":"Untargeted attack against federated recommendation systems via poisonous item embeddings and the defense","authors":"Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Sanshi Lei Yu, Zaixi Zhang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yue et al., 2022] Qin Yue, Jiye Liang, Junbiao Cui, and Liang Bai. Dual bidirectional graph convolutional net- works for zero-shot node classification. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Dis- covery and Data Mining, pages 2408-2417, 2022.","title":"Dual bidirectional graph convolutional networks for zero-shot node classification","authors":"Qin Yue, Jiye Liang, Junbiao Cui, Liang Bai","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhou et al., 2003] Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. Advances in neural information processing systems, 16, 2003.","title":"Learning with local and global consistency","authors":"Zhou","year":"2003","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhu et al., 2003] Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International conference on Machine learning (ICML-03), pages 912-919, 2003.","title":"Semi-supervised learning using gaussian fields and harmonic functions","authors":"Xiaojin Zhu, Zoubin Ghahramani, John D Lafferty","year":"2003","venue":"Proceedings of the 20th International conference on Machine learning (ICML-03)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}