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Learning with Graphs/Networks

We design machine learning models for solving diverse graph/network problems, such as attributed network embedding, knowledge graph alignment, knowledge graph completion, dynamic graph modeling, spatial-temporal network embedding and influential nodes tracking in dynamic networks.

  • Rui Cai, Shichao Pei, Xiangliang Zhang. Zero-Shot Relational Learning for Multimodal Knowledge Graphs. The 2024 IEEE International Conference on Big Data (Regular paper, acceptance rate: 18.8% 124 out of 660 submissions)
  • Xiaoting Lyu, Yufei Han, Wei Wang, Hangwei Qian, Ivor Tsang, Xiangliang Zhang. Cross-Context Backdoor Attacks against Graph Prompt Learning. Accepted by the 30th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024). Barcelona, Spain, Sunday 25 August 2024 – Thursday 29 August 2024.
  • Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang and Robert Hoehndorf. Neural Multi-hop Logical Query Answering with Concept-level Answers. The 22nd International Semantic Web Conference (ISWC 2023).
  • Shichao Pei, Ziyi Kou, Qiannan Zhang, Xiangliang Zhang. Few-shot Low-resource Knowledge Graph Completion with Multi-view Task Representation Generation. Accepted by KDD 2023. Long Beach, CA. Aug 6-13, 2023. (acceptance rate = 313/1416 = 22%)
  • Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, Xiangliang Zhang. Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation. Accepted by KDD 2023. Long Beach, CA. Aug 6-13, 2023. (acceptance rate = 313/1416 = 22%)
  • Shichao Pei, Qiannan Zhang, Xiangliang Zhang. Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation. Findings of ACL 2023.
  • Zhichun Guo,  Kehan Guo, Bozhao Nan, Yijun Tian, Roshni Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh Chawla. Graph-based Molecular Representation Learning. Accepted by The 32nd International Joint Conference on Artificial Intelligence (IJCAI-23). 19th-25th August 2023. Macao, S.A.R. (Survey)
  • Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang Zhang and Scott Sanner LogicRec: Recommendation with Users’ Logical Requirements. Accepted by SIGIR 2023 (Short paper, Acceptance rate = 154/613 = 25.12%)
  • Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh Chawla. Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency. Accepted (notable-top-25%) by the 11th International Conference on Learning Representations (ICLR 2023). Kigali, Rwanda, May 1-5, 2023.
  • Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh Chawla, Xiangliang Zhang. Cross-domain Few-shot Graph Classification with a Reinforced Task Coordinator. Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023). Feb 7-14, 2023 Washington DC. (Acceptance rate = 19.6% （1,721 of 8,777 submissions))
  • Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, Xiangliang Zhang. Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs. ACM International Conference on Web Search and Data Mining (WSDM 2023). Singapore February 27 to March 3, 2023.
  • Qiannan Zhang, Xiaodong Wu, Qiang Yang, Chuxu Zhang, Xiangliang Zhang. Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer. Accepted at KDD 2022 Research Track. August 14-18, 2022, in Washington, DC (Acceptance Rate:  254/1695  = 15%)
  • Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang. Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion. Accepted by the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022). (acceptance rate =15%, 681 out of 4535 submissions).
  • Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh Chawla, Huan Liu. Few-Shot Learning on Graphs: A Survey. The 31st International Joint Conference on Artificial Intelligence – Survey Track. (IJCAI 2022). Early Access on arXiv
  • Shichao Pei, Lu Yu, Guoxian Yu and Xiangliang Zhang. Graph Alignment with Noisy Supervision. Accepted by TheWebConf 2022. (Acceptance rate: 323/1822 =17.7%)
  • Qiannan Zhang, Xiaodong Wu, Qiang Yang, Chuxu Zhang, Xiangliang Zhang. HG-Meta: Graph Meta-learning over Heterogeneous Graphs. Accepted by SIAM International Conference on Data Mining (SDM 2022) acceptance rate: 83/298 = 27.8%).
  • Wei Wang, Xiangyu Wei, Xiaoyang Suo, Bin Wang, Hao Wang, Hong-Ning Dai, Xiangliang Zhang. “HGATE: Heterogeneous Graph Attention Auto-Encoders”. To appear in  IEEE Transactions on Knowledge and Data Engineering (TKDE).
  • Shichao Pei, Lu Yu, Xiangliang Zhang. Set-aware Entity Synonym Discovery with Flexible Receptive Fields.  IEEE Transactions on Knowledge and Data Engineering (TKDE). 2021. Early Access. 10.1109/TKDE.2021.3087532
  • Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, and Xiangliang Zhang. Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation. Accepted by Neural Information Processing Systems (NeurIPS 2020). Virtual-only Conference. (Acceptance rate = 1900 /9454 = 20%).
  • Zaiqiao Meng, Shangsong Liang, Xiangliang Zhang, Richard Mccreadie, Idah Ounis. Jointly Learning Representations of Nodes and Attributes for Attributed Networks. ACM Transactions on Information Systems (TOIS), 38(2), 1–32. 2020.
  • Uchenna​ Akujuobi, Qiannan Zhang, Han Yufei, Xiangliang Zhang. Recurrent Attention Walk for Semi-supervised Classification. In the proceedings of The 13th ACM International WSDM Conference​. Feb 3-7, 2020, Houston, Texas. (Acceptance rate =91/615 = 15%)​ (paper at arXiv)
  • ​​Yujun Chen, Ke Sun, Juhua Pu, Zhang Xiong, Xiangliang Zhang. GraPASA: Parametric Graph Embedding via Siamese Architecture.  Information Sciences 512: 1442-1457 (2020).​​​
  • Yujun Chen, Juhua Pu, Xingwu Liu, Xiangliang Zhang. Gaussian Mixture Embedding of Multiple Node Roles in Networks.  World Wide Web 23(2): 927-950 (2020) [PDF​]
  • Uchenna Akujuobi, Han Yufei, Qiannan Zhang, Xiangliang Zhang. Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification. In the proceedings of 19th IEEE International Conference on Data Mining (ICDM 2019), November 8-11, 2019, Beijing, China (Regular paper, Acceptance  rate= 95/1046 = 9.08%).​ (paper at arXiv​)
  • Yujun Chen, Yuanhong Wang, Yutao Zhang, Juhua Pu,  Xiangliang Zhang. AMENDER: an Attentive and Aggregate Multi-layered Network for Dataset Recommendation. In the proceedings of 19th IEEE International Conference on Data Mining (ICDM 2019), November 8-11, 2019, Beijing, China (Short paper, Acceptance rate= 18.5%).​
  • Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang. Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference.  In the proceedings of by TheWebConf 2019 (previously known as WWW conference),  May 13-17, San Francisco, CA, USA (short paper, acceptance rate ~ 20%) [PDF][Bib][Slides​][Code​].
  • Uchenna Akujuobi, Ke Sun, and Xiangliang Zhang:  Mining top-k Popular Datasets via a Deep Generative Model. In the proceedings of  by IEEE Big Data 2018, Seattle, WA, USA, December 10-13, 2018 (regular paper, acceptance rate = 98/518 =18.9%)[PDF​][Bib][Slides​].
  • Pinghui Wang, Yiyan Qi, Yu Sun, Xiangliang Zhang, Jing Tao, Xiaohong Guan. Approximately Counting Triangles in Large Graph Streams Including Edge Duplicates with a Fixed Memory Usage. In Proceedings of  Very Large Data Bases (VLDB 2017), 11(2): 162-175, 2017 [PDF][Bib].​