Publications

2024

• Yiwei Dai, Hengrui Gu, Ying Wang, Xin Wang*. Mitigate Extrinsic Social Bias in Pre-trained Language Models via Continuous Prompts Adjustment. EMNLP Main Conference, 2024. (Tsinghua University CS-A)

• Hengrui Gu, Kaixiong Zhou, Yili Wang, Ruobing Wang, Xin Wang*. Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion. EMNLP Findings, 2024. (Tsinghua University CS-A)

• Xin Juan, Xiao Liang, Haotian Xue, Xin Wang*. Multi-Strategy Adaptive Data Augmentation for Graph Neural Networks. Expert Systems with Applications, 2024. (Q1)

• Ruobing Wang, Xin He, Hengrui Gu, Xin Wang*. LGCRS: LLM-Guided Representation-Enhancing for Conversational Recommender System. ICANN, 2024. (CCF C)

• Mingchen Sun, YingJi Li, Ying Wang, Xin Wang. Towards Domain-Aware Stable Meta Learning for Out-of-Distribution Generalization. ACM Transactions on Knowledge Discovery from Data, 2024. (CCF B)

• Yili Wang, Haotian Xue, Xin Wang*. A two-stage co-adversarial perturbation to mitigate out-of-distribution generalization of large-scale graph. Expert Systems with Applications, 2024. (Q1)

• Xu Shen, Yili Wang, Kaixiong Zhou, Shirui Pan, Xin Wang*. Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models. KDD, 2024. (CCF-A)

• Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang*. PokeMQA: Programmable knowledge editingfor Multi-hop Question Answering. ACL Main Conference, 2024. (CCF-A)

• Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang. Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models. ACL Findings, 2024. (CCF-A)

• Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, Xin Wang*. Rethinking Independent Cross-Entropy Loss For Graph-Structured Data. ICML, 2024. (CCF-A)

• Yingji Li, Mengnan Du, Rui Song, Xin Wang, Mingchen Sun, Ying Wang. Mitigating Social Biases of Pre-trained Language Models via Contrastive Self-Debiasing with Double Data Augmentation. Artificial Intellignece, 2024. (CCF-A)

• Yiwei Dai, Mingchen Sun, Xin Wang*. Pre-Training Graph Neural Networks via Weighted Meta Learning. IJCNN, 2024. (CCF-C)

• Xin Juan, Kaixiong Zhou, Ninghao Liu, Tianlong Chen, Xin Wang*. Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision. CVPR, 2024. (CCF-A)

• Yili Wang, Kaixiong Zhou, Ninghao Liu, Ying Wang, Xin Wang*. Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models. ICLR, 2024. (Tsinghua University CS-A)

• Zihao Chen, Ying Wang, Fuyuan Ma, Hao Yuanhao, Xin Wang. GPL-GNN: Graph Prompt Learning for Graph Neural Network. Knowledge-based Systems, 2024. (Q1)

2023

• Mingchen Sun, Mengduo Yang, Yingji Li, Dongmei Mu, Xin Wang, Ying Wang. Structural-aware Motif-based Prompt Tuning for Graph Clustering. Information Sciences, 2023. (Q1)

• Ying Wang, Yingji Li, Yue Wu, Xin Wang*. Exploring Multiple Hypergraphs for Heterogeneous Graph Neural Networks. Expert Systems with Applications, 2023. (Q1)

• Xianglin Zuo, Wenqi Chen, Xianduo Song, Xin Wang, Ying Wang. Generating Real-world Hypergraphs via Deep Generative Models. Information Sciences, 2023. (Q1，CCF-B)

• Hengrui Gu, Xin Wang*. LAGCL: Towards Stable and Automated Graph Contrastive Learning. ADMA, 2023. (CCF-C)

• Yingji Li, Mengnan Du, Xin Wang, Ying Wang. Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases. ACL Main Conference, 2023. (CCF-A)

• Xin Juan, Fengfeng Zhou, Wentao Wang, Wei Jin, Jiliang Tang, Xin Wang*. INS-GNN: Improving graph imbalance learning with self-supervision. Information Sciences, 2023. (Q1，CCF-B)

2022

• Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang*. AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022. (CCF-B)

• Rui Miao, YintaoYang, Yao Ma, Xin Juan, Haotian Xue, Jiliang Tang, YingWang, Xin Wang*. Negative Samples Selecting Strategy for Graph Contrastive Learning. Information Sciences, 2022. (Q1, CCF-B)

• Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, Xin Wang*. GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. (CCF-A)

• Kai Guo, Kaixiong Zhou, Xia Hu, Yi Chang, Xin Wang*. Orthogonal Graph Neural Networks. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), 2022. (CCF-A)

• Yintao Yang, Rui Miao, Yili Wang, Xin Wang*. Contrastive Graph Convolutional Networks with Adaptive Augmentation for Text Classification. Information Processing and Management, 59(4): 102946, 2022. (Q1, CCF-B)

• Song Xianduoa#, Wang Xin#, Song Yuyuana, Zuo Xianglin, Wang Ying*. Hierarchical Recurrent Neural Networks for Graph Generation. Information Sciences, 589: 250-264, 2022. (Q1, CCF-B)

2021

• Xin Juan, Meixin Peng, Xin Wang*. Exploring Self-training for Imbalanced Node Classification. International Conference on Neural Information Processing (ICONIP), 2021: 28-36. (CCF-C)

• Ying Wang, Hongji Wang, Xinrui Huan, Xin Wang*. Exploring Graph Capsual Network for Graph Classification. Information Sciences, 581: 932-950, 2021. (Q1, CCF-B)

• Siyuan Guo, Ying Wang, Hao Yuan, Zeyu Huang, Jianwei Chen, Xin Wang*. TAERT: Triple-Attentional Explainable Recommendation with Temporal Convolutional Network. Information Sciences, 567: 185-200, 2021. (Q1, CCF-B)

Before 2020

• Xin Wang, Ying Wang. Attention guide Walk Model in Heterogeneous Information Network for Multi-style Recommendation Explanation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2020: 6275-6282. (CCF-A)

• Xiaoyang Wang, Yao Ma, Wei Jin, Xin Wang, Jiliang Tan, Jian Yu. Traffic Speed Prediction Based on Spatial Temporal Graph Neural Network. In Proceedings of the World Wide Web Conference (WWW), 2020: 1082-1092. (CCF-A)

• Xin Wang, Ying Wang, Wanli Zuo, Yongguo, Cai. Exploring social context for topic identification in short and noisy text. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015, 1868-1874. (CCF-A)

• Ying Wang, Xin Wang, Jiliang Tang, WanliZuo. Modeling status theory in trust prediction. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015, 1875-1881. (CCF-A)

• Ying Wang, Xin Wang*, WanliZuo. Research on trust prediction from a sociological perspective. Journal of Computer Science and Technology (JCST), 2015, 30(4): 843-858. (CCF-B)

• Xin Wang, Ying Wang, Jianhua, Guo. Building trust networks in the absence of trust relations. Frontiers of Information Technology & Electronic Engineering (FITEE), 2017, 18 (10): 1591-1600. (Q2)

• Yunzhi Ling, Ying Wang, Xin Wang, Yunhao Ling. Exploring Common and Label-Specific Features for Multi-Label Learning with Local Label Correlations. IEEE Access, 2020. (Q2)

• Xin Wang, Ying Wang, Hongbin Sun. Exploring the combination of dempster- shafer theory and neural network for predicting trust and distrust. Computational Intelligence and Neuroscience, 2016, 5403105: 1-12.

• Xin Wang, Wanli Zuo, Ying Wang. A novel approach to word sense disambiguation based on topical and semantic association. The Scientific World Journal, 2013, 586327: 1-8.

• 王鑫, 王英, 左万利. 基于交互意见和地位理论的符号网络链接预测模型研究. 计算机研究与发展, 2016(4): 764-775. (CCF-A类中文)

• 吴越, 王英, 王鑫, 徐正祥, 李丽娜. 基于超图卷积的异质网络半监督节点分类. 计算机学报, 2021, 44 (11): 2248-2260. (CCF-A类中文)

• 王英, 王鑫, 左万利. 基于社会学理论的信任关系预测模型研究. 软件学报, 2014, 25(12): 2893-2904. (CCF-A类中文)

• 孙小婉, 王英, 王鑫, 孙玉东. 面向双注意力网络的特定方面情感分析模型. 计算机研究与发展, 2019, 56(11): 2384-2395. (CCF-A类中文)

• 王英, 左祥麟, 左万利, 王鑫. 基于本体的Deep Web查询接口集成. 计算机研究与发展, 2012, 49(11): 2383-2394. (CCF-A类中文)