@article{gao2026scsimulator,author={Gao, Shenghan and Wang, Junye and Xiong, Junjie and Jiang, Yun and Fang, Yun and Hu, Qifan and Liu, Baolong and Li, Quan},title={SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through {LLM}-driven Multi-Agent Simulation},booktitle={Proceedings of the 31st International Conference on Intelligent User Interfaces},series={IUI '26},year={2026},pages={1--23},numpages={23},address={Paphos, Cyprus},publisher={Association for Computing Machinery},doi={10.1145/3742413.3789061},}
CHI
CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment
Yang Ouyang*, Shenghan Gao*, Ruichuan Wang, Hailiang Zhu, Yuheng Shao, Xiaoyu Gu, and Quan Li
@article{ouyang2026commsense,author={Ouyang, Yang and Gao, Shenghan and Wang, Ruichuan and Zhu, Hailiang and Shao, Yuheng and Gu, Xiaoyu and Li, Quan},title={CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment},booktitle={Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems},series={CHI '26},year={2026},pages={1--30},numpages={30},address={Barcelona, Spain},publisher={Association for Computing Machinery},doi={10.1145/3772318.3790530},}
2025
TVCG
From Requirement to Solution: Unveiling Problem-Driven Design Patterns in Visual Analytics
@article{10874217,author={Wu, Yuchen and Gao, Shenghan and Zhang, Shizhen and Dou, Xiaofeng and Wang, Xingbo and Li, Quan},journal={IEEE Transactions on Visualization and Computer Graphics},title={From Requirement to Solution: Unveiling Problem-Driven Design Patterns in Visual Analytics},year={2025},volume={},number={},pages={1-18},keywords={Problem-solving;Data visualization;Data models;Guidelines;Decision making;Terminology;Encoding;Computational modeling;Visual analytics;Training;Visual Analytics;Design Patterns;Problem-Solving;Typology},doi={10.1109/TVCG.2025.3538768},}
UIST
QueryGenie: Making LLM-Based Database Querying Transparent and Controllable
Longfei Chen, Shenghan Gao, Shiwei Wang, Ken Lin, Yun Wang, and Quan Li
In Adjunct Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology, 2025
@inproceedings{10.1145/3746058.3758982,author={Chen, Longfei and Gao, Shenghan and Wang, Shiwei and Lin, Ken and Wang, Yun and Li, Quan},title={QueryGenie: Making LLM-Based Database Querying Transparent and Controllable},year={2025},isbn={9798400720369},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3746058.3758982},doi={10.1145/3746058.3758982},booktitle={Adjunct Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology},articleno={49},numpages={4},keywords={User interface, large language model, data query},series={UIST Adjunct '25'}}
ICWSM
Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach
Laixin Xie, Ying Zhang, Xiyuan Wang, Shiyi Liu*, Shenghan Gao*, Xingxing Xing, Wei Wan, Haipeng Zhang, and Quan Li
Proceedings of the International AAAI Conference on Web and Social Media, Jun 2025
@article{xie2025influence,title={Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach},volume={19},url={https://ojs.aaai.org/index.php/ICWSM/article/view/35919},doi={10.1609/icwsm.v19i1.35919},abstractnote={Influence Maximization (IM) in temporal graphs focuses on identifying influential ``seeds’’ that are pivotal for maximizing network expansion. We advocate defining these seeds through Influence Propagation Paths (IPPs), which is essential for scaling up the network. Our focus lies in efficiently labeling IPPs and accurately predicting these seeds, while addressing the often-overlooked cold-start issue prevalent in temporal networks. Our strategy introduces a motif-based labeling method and a tensorized Temporal Graph Network (TGN) tailored for multi-relational temporal graphs, bolstering prediction accuracy and computational efficiency. Moreover, we augment cold-start nodes with new neighbors from historical data sharing similar IPPs. The recommendation system within an online team-based gaming environment presents subtle impact on the social network, forming multi-relational (i.e., weak and strong) temporal graphs for our empirical IM study. We conduct offline experiments to assess prediction accuracy and model training efficiency, complemented by online A/B testing to validate practical network growth and the effectiveness in addressing the cold-start issue.},number={1},journal={Proceedings of the International AAAI Conference on Web and Social Media},author={Xie, Laixin and Zhang, Ying and Wang, Xiyuan and Liu, Shiyi and Gao, Shenghan and Xing, Xingxing and Wan, Wei and Zhang, Haipeng and Li, Quan},year={2025},month=jun,pages={2062-2075},}
2023
TVCG/VIS
LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce
Yuchen Wu, Yuansong Xu, Shenghan Gao, Xingbo Wang, Wenkai Song, Zhiheng Nie, Xiaomeng Fan, and Quan Li
IEEE Transactions on Visualization and Computer Graphics, 2023
@article{10295389,author={Wu, Yuchen and Xu, Yuansong and Gao, Shenghan and Wang, Xingbo and Song, Wenkai and Nie, Zhiheng and Fan, Xiaomeng and Li, Quan},journal={IEEE Transactions on Visualization and Computer Graphics},title={LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce},year={2023},volume={30},number={1},pages={1117-1127},keywords={Electronic commerce;Streaming media;Visual analytics;Interviews;Forecasting;Behavioral sciences;Analytical models;Livestream E-commerce;Visual Analytics;Multimodal Video Analysis;Marketing Strategy;Time-series Modeling},doi={10.1109/TVCG.2023.3326911},}