Tsinghua University-KEG (Knowledge Engineering Group) LabThere are KEGer's papers.
Si Zhang,Hanghang Tong,Jie Tang, Jiejun Xu,Wei Fan
ACM Transactions on Knowledge Discovery from Data, no. 4 (2020): 1-26
The multi-sourced and incomplete characteristics often co-exist in many real networks, the state-of-the-arts have been largely addressing network alignment and network completion problems in parallel
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.1666-1676, (2020)
We study the effect of negative sampling, a practical approach adopted in the literature of graph representation learning
Cited by1BibtexViews685Links
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Yukuo Cen,Jing Zhang, Gaofei Wang, Yujie Qian, Chuizheng Meng, Zonghong Dai,Hongxia Yang,Jie Tang
IEEE Transactions on Knowledge and Data Engineering, no. 5 (2020): 1024-1035
Experimental results on four genres of real-world datasets show that the proposed method significantly outperforms comparison methods
Cited by1BibtexViews305Links
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national conference on artificial intelligence, (2020)
We contribute a supplement image dataset for Event Detection benchmark ACE2005, which can be further analyzed in related tasks such as event extraction
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Jifan Yu,Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang,Wenzheng Feng, Junyi Luo, Chenyu Wang,Lei Hou,Juanzi Li,Zhiyuan Liu,Jie Tang
ACL, pp.3135-3142, (2020)
We present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource
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ACL, pp.5887-5897, (2020)
We introduce the proposed Enrichment Knowledge Distillation model, which leverages open-domain trigger knowledge to improve Event Detection
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.2942-2951, (2020)
Recommender systems start a new phase owing to the rapid development of deep learning
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KDD 2020, (2020)
We present Graph Contrastive Coding, which is a graph-based contrastive learning framework to pre-train graph neural networks from multiple graph datasets
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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Re..., pp.79-88, (2020)
We investigate the problem of the knowledge concept recommendation in massive open online courses system, which is often overlooked by massive open online courses recommendation system
Cited by0BibtexViews276Links
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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event..., pp.1150-1160, (2020)
We study graph representation learning with the goal of characterizing and transferring structural features in social and information networks
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Meeting of the Association for Computational Linguistics, (2019)
Our implementation based on BERT and graph neural network obtains state-of-art results on HotpotQA dataset, which shows the efficacy of our framework
Cited by39BibtexViews629Links
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We focus on embedding learning for Attributed MHEN, where different types of nodes might be linked with multiple different types of edges, and each node is associated with a set of different attributes
Cited by28BibtexViews356Links
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WWW '19: The Web Conference on The World Wide Web Conference WWW 2019, pp.1509-1520, (2019)
To address the scalability challenges faced by the NetMF model, we propose to study large-scale network embedding as sparse matrix factorization
Cited by27BibtexViews402Links
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Wenzheng Feng,Jie Tang,Tracy Xiao Liu
AAAI, pp.517-524, (2019)
We propose a contextaware feature interaction network to predict users’ dropout probability
Cited by26BibtexViews137Links
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CVPR, (2019): 8376-8384
We proposed X neural modules that allows visual reasoning over scene graphs, represented by different detection qualities
Cited by21BibtexViews138Links
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IJCAI, pp.4278-4284, (2019)
We propose ProNE—a fast and scalable network embedding approach
Cited by13BibtexViews382Links
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EMNLP/IJCNLP (1), pp.2723-2732, (2019)
We propose a semi-supervised entity alignment method Knowledge Embedding model and Cross-Graph model that combines the knowledge embedding model and graph-based model
Cited by11BibtexViews124
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arXiv: Learning, (2019): 1-1
We proposed a new learning method, named graph adversarial training, which additional accounts for relation between examples as compared to standard adversarial training
Cited by9BibtexViews253Links
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AAAI, (2019)
We propose DeepChannel, consisting of a deep neural network-based channel model and an iterative extraction strategy, for extractive document summarization
Cited by9BibtexViews1488Links
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This paper addresses such problems using few-shot learning and meta learning
Cited by8BibtexViews155Links
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Keywords
Social NetworkSemantic WebSocial InfluenceCorrelationData MiningComputer ScienceSocial NetworksInternetFactor GraphGraph Theory
Authors
Jie Tang
Paper 268
Juanzi Li
Paper 146
Jing Zhang
Paper 32
Kehong Wang
Paper 26
Lei Hou
Paper 20
Yuxiao Dong
Paper 13
Ying Ding
Paper 13
Bin Xu
Paper 13
Sen Wu
Paper 12
Bangyong Liang
Paper 11