ArnetMiner: extraction and mining of academic social networks
- Jie Tang, Jing Zhang, Limin Yao, Juan-Zi Li, Li Zhang, Z. Su
- Computer ScienceKnowledge Discovery and Data Mining
- 24 August 2008
The architecture and main features of the ArnetMiner system, which aims at extracting and mining academic social networks, are described and a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues is proposed.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
- J. Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang
- Computer ScienceWeb Search and Data Mining
- 9 October 2017
The NetMF method offers significant improvements over DeepWalk and LINE for conventional network mining tasks and provides the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
- J. Qiu, Qibin Chen, Jie Tang
- Computer ScienceKnowledge Discovery and Data Mining
- 17 June 2020
Graph Contrastive Coding (GCC) is designed --- a self-supervised graph neural network pre-training framework --- to capture the universal network topological properties across multiple networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations.
Social influence analysis in large-scale networks
- Jie Tang, Jimeng Sun, Chi Wang, Zi Yang
- Computer ScienceKnowledge Discovery and Data Mining
- 28 June 2009
Topical Affinity Propagation (TAP) is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework and can take results of any topic modeling and the existing network structure to perform topic-level influence propagation.
Towards Knowledge-Based Recommender Dialog System
- Qibin Chen, Junyang Lin, Jie Tang
- Computer ScienceConference on Empirical Methods in Natural…
- 1 August 2019
A novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System, integrates the recommender system and the dialog generation system and shows that the two systems can bring mutual benefits to each other.
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
- Juan-Zi Li, Jie Tang, Yi Li, Qiong Luo
- Computer ScienceIEEE Transactions on Knowledge and Data…
- 1 August 2009
This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM, and proposes a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and a strategy selection method to automatically combine the matching strategies based on two estimated factors.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
- Wenzheng Feng, Jie Zhang, Jie Tang
- Computer ScienceNeural Information Processing Systems
- 22 May 2020
In GRAND, a simple yet effective framework for semi-supervised learning on graphs that first design a random propagation strategy to perform graph data augmentation, then leverages consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations.
COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency
- Yutao Zhang, Jie Tang, Zhilin Yang, J. Pei, Philip S. Yu
- Computer ScienceKnowledge Discovery and Data Mining
- 10 August 2015
An efficient subgradient algorithm is developed to train the model by converting the original energy-based objective function into its dual form, and it is demonstrated that applying the integration results produced by the method can improve the accuracy of expert finding, an important task in social networks.
Representation Learning for Attributed Multiplex Heterogeneous Network
- Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang
- Computer ScienceKnowledge Discovery and Data Mining
- 5 May 2019
Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice, and the theoretical analysis of the proposed framework gives its connection with previous works and proving its better expressiveness.
User-level sentiment analysis incorporating social networks
- Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, M. Zhou, Ping Li
- Computer ScienceKnowledge Discovery and Data Mining
- 21 August 2011
It is shown that information about social relationships can be used to improve user-level sentiment analysis and incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.
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