Tutorial-ECML-PKDD-2020

https://www.aminer.cn/grla_ecmlpkdd2020

Tutorial Outline

Part 1 Introduction [Slides][https://www.dropbox.com/s/f51y9afnn25c2dp/ecmlpkdd2020-tutorial-part0-intro.pdf?dl=0]

Part 2 Network Embeddings [Slides]

2.1 Conventional network analysis

  Node classification
  Social tie & link prediction

2.2 Network embeddings

  Embedding models
  Theoretical understanding
  Billion-scale graph embedding

Part 3 Graph Neural Networks & Pre-Training [Slides]

3.1 GNNs Fundamentals

  Graph convolution & attention
  Graph GAN
  Dynamic graph representation
  Heterogeneous graph representation

3.2 GNN Pre-Training

  Generative pre-training
  Contrastive pre-training

3.3 Large-Scale GNN Applications

Part 4 Conclusion [Slides]

4.1 Open Graph Data and Benchmarks

4.2 Summary

Related Publications

• Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. Self-supervised Learning: Generative or Contrastive. [PDF] [https://arxiv.org/pdf/2006.08218.pdf]
• Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. GCC: Graph Contrastive Coding for Graph Neural Network - Pre-Training. In Proceedings of the Twenty-Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20). [PDF] [data & code]
• Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In Proceedings of the Twenty-Sixth ACM - SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20). [PDF] [data & code]
• Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang. Understanding Negative Sampling in Graph Representation Learning. In Proceedings of the Twenty-Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20). [PDF] [data & code]
• Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. Controllable Multi-Interest Framework for Recommendation. In Proceedings of the Twenty-- Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20). [PDF]
• Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun. Heterogeneous Graph Transformer. In Proceedings of the Web Conference 2020 (WWW'20). [PDF] [data & code]
• Jibing Gong, Shen Wang, Jinlong Wang, Hao Peng, Wenzheng Feng, Dan Wang, Yi Zhao, Huanhuan Li, Jie Tang, and Philip Yu. Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View. In Proceedings of the 43th International ACM SIGIR Conference on Research and Development in - Information Retrieval (SIGIR'20). [PDF]
• Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun and Jie Tang. Heterogeneous Network Representation Learning. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20). [PDF]
• Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. In Proceedings of the 57th Annual - Meeting of the Association of Computational Linguistics (ACL'19). [PDF] [data & code]
• Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, and Kuansan Wang. OAG: Toward Linking Large-scale - Heterogeneous Entity Graphs. In Proceedings of the Twenty-Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19). [PDF] [data] [code] [code]
• Xichen Ding, Jie Tang, Tracy Liu, Cheng Xu, Yaping Zhang, Feng Shi, Qixia Jiang and Dan Shen. Infer Implicit Contexts in Real-time Online-to-Offline - Recommendation. In Proceedings of the Twenty-Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19). [PDF]
• Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou and Jie Tang. Representation Learning for Attributed Multiplex Heterogeneous Network. In Proceedings of the Twenty-Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19). [PDF] [data & code]
• Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou and Jie Tang. Towards Knowledge-Based Personalized Product Description Generation in E-commerce. In Proceedings of the Twenty-Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19). [PDF] [data & code]
• Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou and Jie Tang. Sequential Scenario-Specific Meta Learner for Online Recommendation. In Proceedings of the - Twenty-Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19). [PDF]
• Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. In Proceedings of the Web Conference 2019 (WWW'19) (accepted). [PDF] [data & code]
• Yu Han, Jie Tang, and Qian Chen. Network Embedding under Partial Monitoring for Evolving Networks. In Proceedings of the 28th International Joint Conference on - Artificial Intelligence (IJCAI'19). [PDF] [Slides_PPT] [Slides_PDF]
• Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, and Ming Ding. ProNE: Fast and Scalable Network Representation Learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19). [PDF] [data & code]
• Yifeng Zhao, Xiangwei Wang, Hongxia Yang, Le Song, and Jie Tang. Large Scale Evolving Graphs with Burst Detection. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19). [PDF]
• Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua. Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. IEEE Transaction on Knowledge and Data Engineering (TKDE), 2019 (accepted). [PDF]
• Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou and Jie Tang. Sequential Scenario-Specific Meta Learner for Online Recommendation. In Proceedings of the Twenty-Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19).
• Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Social Influence Prediction with Deep Learning. In Proceedings of the Twenty- Forth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18). [PDF] [poster] [data & code] [video]
• Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM'18). [PDF] [Slides] [code]