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2020 – today
- 2024
[j5]Yuyang Gao
, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, Liang Zhao:
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. ACM Comput. Surv. 56(7): 188:1-188:39 (2024)- [j4]Bing Lv, Junji Jiang, Likang Wu, Hongke Zhao:
Team formation in large organizations: A deep reinforcement learning approach. Decis. Support Syst. 187: 114343 (2024) - [j3]Junji Jiang, Chen Ling, Hongyi Li, Guangji Bai, Xujiang Zhao, Liang Zhao:
Quantifying uncertainty in graph neural network explanations. Frontiers Big Data 7 (2024) - [j2]Tanmoy Chowdhury, Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Liang Zhao:
Deep graph representation learning influence maximization with accelerated inference. Neural Networks 180: 106649 (2024) - 2023
- [j1]Junji Jiang, Likang Wu, Hongke Zhao, Hengshu Zhu, Wei Zhang:
Forecasting movements of stock time series based on hidden state guided deep learning approach. Inf. Process. Manag. 60(3): 103328 (2023) - [c7]Junji Jiang, Hongke Zhao, Ming He, Likang Wu, Kai Zhang, Jianping Fan:
Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation. CIKM 2023: 965-975 - [c6]Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Renhao Xue, James Song, Meikang Qiu, Liang Zhao:
Deep Graph Representation Learning and Optimization for Influence Maximization. ICML 2023: 21350-21361 - [c5]Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen:
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification. IJCAI 2023: 2361-2369 - [i6]Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Lukas Xue, James Song, Meikang Qiu, Liang Zhao:
Deep Graph Representation Learning and Optimization for Influence Maximization. CoRR abs/2305.02200 (2023) - [i5]Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen:
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification. CoRR abs/2308.08563 (2023) - 2022
- [c4]Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, Liang Zhao:
DeepGAR: Deep Graph Learning for Analogical Reasoning. ICDM 2022: 1065-1070 - [c3]Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao:
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. KDD 2022: 1010-1020 - [c2]Junxiang Wang, Junji Jiang, Liang Zhao:
An Invertible Graph Diffusion Neural Network for Source Localization. WWW 2022: 1058-1069 - [i4]Junxiang Wang, Junji Jiang, Liang Zhao:
An Invertible Graph Diffusion Neural Network for Source Localization. CoRR abs/2206.09214 (2022) - [i3]Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao:
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems. CoRR abs/2206.12327 (2022) - [i2]Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, Liang Zhao:
DeepGAR: Deep Graph Learning for Analogical Reasoning. CoRR abs/2211.10821 (2022) - [i1]Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, Liang Zhao:
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning. CoRR abs/2212.03954 (2022) - 2021
- [c1]Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao:
GraphGT: Machine Learning Datasets for Graph Generation and Transformation. NeurIPS Datasets and Benchmarks 2021
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