Shenghua Liu - Homepage

Biography

Publication

Softwares

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He is now a Professor at Institute of Computing Technology, Chinese Academy of Sciences.

He spent his one-year sabbatical at Computer Science Department, Carnegie Mellon University (CMU), as a research scholar. He was hosted and supervised by Professor Christos Faloutsos, 2016-2017.

He received his Ph.D. degree from Computer Science & Technology Department, Tsinghua University in 2010, and was funded by hosting Professor Lei He to visit Electronic Engineering Department, University of California, Los Angeles (UCLA) as a Ph.D. student, 2006-2007. He is in consequence listed as one of the Alumni in Academia of Electrical & Computer Engineering, UCLA.

He focuses on research areas such as big graph mining, large language models (LLMs), and scalable algorithm design. The arising of LLMs drives most of his interests to trustworthy foundation models, and graph LLMs which brings LLM the ability to understand grpahs, think and model with graphs. With graph LLMs, real world complex and combination problems can be well and trustworthily solved by AGI. His expertise is applied to interpreting complex patterns across diverse domains, including online user behavior within social platforms and e-commerce, as well as various networked systems ranging from academic collaborations, supply chain, financial transactions and biological networks. The featured works are published on IEEE TKDE, ACM TKDD, and proceedings of top-tier conferences such as AAAI, ICLR, ACL, CIKM, WSDM, ICDM, and SDM. Some of the publications are recognized as ASP-DAC 2010 best paper candicate, ECML-PKDD 2020 best student DM paper award.

Honors and Awards

Best student DM paper award, ECML-PKDD 2020. SpecGreedy: Unified Dense Subgraph Detection, [code]
Outstanding dissertation, Tsinghua University, 2010. [ slides ][ demo1, demo2 ]
Microsoft Research Asia (MSRA) fellowship, 2009.

2025

[C] Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Junfeng Fang, Hongcheng Gao, Shiyu Ni, Xueqi Cheng. Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness, The Thirteenth International Conference on Learning Representations, ICLR, Singapore, 2025 (accept)
[C] Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Jiayi Mao, and Xueqi Cheng. "Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak. In Proceedings of the 31st International Conference on Computational Linguistics (Coling 2025), Jan 2025:2144–2162.

2024

[C] Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, and Xueqi Cheng, SLANG: New Concept Comprehension of Large Language Models, In Proc. of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), 2024:12559-12575.
[C] Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Hongcheng Gao, Yilong Xu, and Xueqi Cheng, Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities, In Proc. of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024:11071-11083.
[C] Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, and Xueqi Cheng, LPNL: Scalable Link Prediction with Large Language Models, In Proc. of the Findings of the Association for Computational Linguistics (ACL 2024), 2024:3615–3625.
[J] Wenjie Feng, Li Wang, Bryan Hooi, See-Kiong Ng, and Shenghua Liu, Interrelated Dense Pattern Detection in Multilayer Networks, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 36, Nov. 2024, pp. 6462-6476.
[J] Houquan Zhou, Shenghua Liu, Huawei Shen, Xueqi Cheng, Node Embedding Preserving Graph Summarization , ACM Transactions on Knowledge Discovery from Data (TKDD), Vol 18, Issue 6, No. 145, pp. 1-19, April 2024.
[C] Houquan Zhou, Shenghua Liu, Huawei Shen, Xueqi Cheng, Graph Summarization for Preserving Spectral Characteristics , In Proc. of the SIAM International Conference on Data Mining (SDM24), pp. 271- 279, 2024.

2023

[J] Wenjie Feng, Shenghua Liu, Danai Koutra, Xueqi Cheng, Unified Dense Subgraph Detection: Fast Spectral Theory based Algorithms, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 36, Issue 3, pp. 1356 – 1370, March 2024 (pub date: 17 July 2023).
[C] Houquan Zhou, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng, A Provable Framework of Learning Graph Embeddings via Summarization, AAAI Conference on Artificial Intelligence (AAAI) 2023.
[J] Wenjie Feng, Shenghua Liu, Xueqi Cheng, Hierarchical Dense Pattern Detection in Tensor, ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 6, Article No.: 81, pp 1–29, 2023
[C] Shengbo Tong, Yuyang Xie, Shenghua Liu, Wenjian Yu, Jixuan Cai. W3Detector: Detecting fraudulent online sellers based on temporal and spacial information. ICMLA 2023.

2022

[C] Xiaobing Sun, Wenjie Feng, Shenghua Liu, Yuyang Xie, Siddharth Bhatia, Bryan Hooi, Wenhan Wang, Xueqi Cheng. MonLAD: Money Laundering Agents Detection in Transaction Streams, In WSDM'22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining WSDM, pp.976–986, Feb 2022. [ code ]
[J] Shenghua Liu, Bin Zhou, Quan Ding, Bryan Hooi, Zhengbo Zhang, Huawei Shen, Xueqi Cheng, Time Series Anomaly Detection with Adversarial Reconstruction Networks , IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 35, Issue 4, 01 April 2023, pp.4293-4306. [ code ]

2021

[J] Xueqi Cheng, Shenghua Liu, Xiaoqian Sun, Zidong Wang, Houquan Zhou, Yu Shao, Huawei Shen, Combating emerging financial risks in the big data era: A perspective review , Fundamental Research, Vol 1, Issue 5, Sep 2021, pp.595-606. [ web ]
[C] Xiaobing Sun, Jiabao Zhang, Qiming Zhao, Shenghua Liu, Jinglei Chen, Ruoyu Zhuang, Huawei Shen, and Xueqi Cheng. CubeFlow: Money Laundering Detection with Coupled Tensors, In PAKDD, pp.78-90. 2021. [ vedio ] [ code ]
[C] Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, Huawei Shen, Wenjian Yu, Xueqi Cheng, AugSplicing: Synchronized Behavior Detection in Streaming Tensors, Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) 2021. (acceptance rate: 21%) [arxiv]
[C] Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen and Xueqi Cheng, DPGS: Degree-Preserving Graph Summarization, In Proc. of the SIAM International Conference on Data Mining (SDM21), pp.280-288, 2021. (acceptance rate: 21.25%. DPGS summarizes (compresses) a big graph to a small one for graph learnings, e.g. GNN, keeping a spectral bound of compression. ) [ appendix ] [ arxiv ] [ code ]

2020

[C] Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, and Bryan Hooi, Provably Robust Node Classification via Low-Pass Message Passing, In proc. of IEEE International Conference on Data Mining (ICDM), Sorrento, Italy, November 17-20, 2020. ( acceptance rate 9.8% for regular papers. Theoretically upper-bounded under adversarialy attacks; Easy-to-plugin module for graph neural networks; As robust as in linear of attack budget, and as accurate as neural networks. ) [ slides ]
[C] Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng, " SpecGreedy: Unified Dense Subgraph Detection ", In proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Belgium, Sept 2020. (Best student DM paper award. Acceptance rate of 19%. Verified on 40 real-world networks, and a 1.47-billion-edge graph )
[J] Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, and Xueqi Cheng. EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection , Future Generation Computer Systems, Vol 115, Feb 2021, pp.236-250.
[C] Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng. " FlowScope: Spotting Money Laundering Based on Graphs ," Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) 2020. [ appendix ] [ code ] [ slides ]

2019

[J] Shenghua Liu , Huawei Shen, Houdong Zheng, Xueqi Cheng, Xiangwen Liao, "CT LIS: Learning Influences and Susceptibilities through Temporal Behaviors", ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 13, No. 6, Article 57, 2019.
[C] Wenjie Feng, Shenghua Liu, Huawei Shen, and Xueqi Cheng, CatchCore: Catching Hierarchical Dense Subtensor, In Proc. of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019), 2019. [ code ]
[C] Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye. BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, International Joint Conference on Artificial Intellince (IJCAI) 2019, pp4433-4439. [ code ]
[C] Bryan Hooi, Kijung Shin, Shenghua Liu, Christos Faloutsos, SMF: Drift-Aware Matrix Factorization with Seasonal Patterns, In Proc. of the SIAM International Conference on Data Mining (SDM19), pp.621-629, 2019.
[C] Dingcheng Yang, Wenjian Yu, Junhui Deng, Shenghua Liu. Optimal Algorithm for Profiling Dynamic Arrays with Finite Values, In Proc. of the 22nd International Conference on Extending Database Technology (EDBT), 26-29 Mar 2019.
[J] Shenghua Liu, Bryan Hooi, Christos Faloutsos, A Contrast Metric for Fraud Detection in Rich Graphs, IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 31, Issue 12, 1 Dec 2019, pp.2235-2248.
[C] Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, Xueqi Cheng, Beyond outliers and on to micro-clusters: Vision-guided anomaly detection, In Proc. of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), 2019, Macau, China, pp541-554. [ code ]
[C] Jiabao Zhang, Shenghua Liu, Wenjian Yu, Wenjie Feng, Xueqi Cheng, EigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation, In Proc. of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), 2019, Macau, China, pp501-513. [ slides ]

2018

[C] Pudi Chen, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, Xueqi Cheng, NeuCast: Seasonal Neural Forecast of Power Grid Time Series, In Proc. of the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18), Stockholm, Sweden, July 13-19, 2018, pp3315-3321. [ code ]
[CW] Wenjie Feng, Shenghua Liu, Christos Faloutsos, Bryan Hooi, Huawei Shen, Xueqi Cheng, EagleMine: Vision-Guided Mining in Large Graphs, ACM SIGKDD 2018, ODD Workshop on Outlier Detection De-constructed, August 20th, London UK. [ code ]
[J] Chuan Shi, Jian Liu, Yiding Zhang, Binbin Hu, Shenghua Liu, Philip S. Yu, MFPR: A Personalized Ranking Recommendation with Multiple Feedback. ACM Transactions on Social Computing, Vol.1, No.2, Article 7, 2018.

2017

[C] Shenghua Liu, Bryan Hooi, and Christos Faloutsos, "HoloScope: Topology-and-Spike Aware Fraud Detection," In Proc. of ACM International Conference on Information and Knowledge Management (CIKM), Singapore, 2017, pp.1539-1548. [slides] [ code ]
[C] Bryan Hooi, Shenghua Liu, Asim Smailagic, and Christos Faloutsos, “BEATLEX: Summarizing and Forecasting Time Series with Patterns,” The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 3-19, Skopje, Macedonia, 2017. [ code ]
[C] Pedro Costa, Aurelio Campilho, Bryan Hooi, Asim Smailagic, Kris Kitani, Shenghua Liu, Christos Faloutsos, and Adrian Galdran, “EyeQual: Accurate, Explainable, Retinal Image Quality Assessment”, in proc. of 16th IEEE International Conference On Machine Learning and Applications (ICMLA), Cancun, Mexico, 2017.
[CW] Shenghua Liu, Bryan Hooi, and Christos Faloutsos, "Fraud Detection using Graph Topology and Temporal Spikes," In Proc. of the 13th international workshop on MLG, KDD, Halifax, Nova Scotia, Canada, 2017.
[C] Shenghua Liu, Houdong Zheng, Xiangwen Liao, Huawei Shen, and Xueqi Cheng: Learning Concise Representations of Users' Influences through Online Behaviors, in Proc. the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Austrila, Aug. 2017, pp.2351-2357.
[C] Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao and Xueqi Cheng. Cascade dynamics modeling with attention-based recurrent neural network, In Proc. of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia, 2017, pp.2985-2991.
[C] Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, and Yaohang Li: Single-pass PCA of large high-dimensional data, In Proc. of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne, Austrila, Aug. 2017, pp.3350-3356. [ code ]
[C] Yongqing Wang, Shenghua Liu, Huawei Shen, Jinhua Gao and Xueqi Cheng: Marked Temporal Dynamics Modeling based on Recurrent Neural Network, In Proc. of the 21rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), Korea, 2017, pp.786-798.
[C] Jian Liu, Chuan Shi, Binbin Hu, Shenghua Liu, Philip S. Yu. Personalized Ranking Recommendation via Integrating Multiple Feedbacks. In Proc. of the 21rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), Korea, 2017, pp.131-143.

2016

[C] Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin and Xueqi Cheng, Predict Anchor Links across Social Networks via an Embedding Approach, In Proc. of the 25th International Joint Conference on Artificial Intelligence IJCAI-16, pp. 1823-1829, July 9-15, New York City, New York, USA.
[O] Shenghua Liu and Xueqi Cheng, Social Media Sentiment Analysis: opinions, users, and behaviors, in Communication of Chinese Association for Artificial Intelligence (CAAI), Special Issue on Social Media Analysis, 2016:12-17.
[C] Jinhua Gao, Huawei Shen, Shenghua Liu, Xueqi Cheng, Modeling and predicting retweeting dynamics via a mixture process, In Proc. of the 25th international conference on World Wide Web (WWW) (Companion Volume), 2016. [link]

2015

[J] Shenghua Liu, Xueqi Cheng, Fuxin Li, Fangtao Li, “TASC: Topic-Adaptive Sentiment Classification on Dynamic Tweets,” IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 27, No. 6, June 2015, pp. 1696-1709.
[C] Yongqing Wang, Huawei Shen, Shenghua Liu and Xueqi Cheng. Learning user-specific latent influence and susceptibility from information cascades. The 29th AAAI Conference on Artificial Intelligence (AAAI-15). Austin, USA, 2015: 477-483.

2014

[C] Shenghua Liu, Xueqi Cheng, Fangtao Li, “Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure,” In Proc. of Twenty-Eighth Conference on Artificial Intelligence (AAAI-14), 2014: 1235-1241.
[C] Fangtao Li, Sheng Wang, Shenghua Liu, Ming Zhang, “SUIT: A Supervised User-Item based Topic model for Sentiment Analysis,” In Proc. of Twenty-Eighth Conference on Artificial Intelligence (AAAI-14), 2014:1636-1642.
[C] Zhankun Huang, Shenghua Liu, Xueqi Cheng, Pan Du, “Ranking Tweets with Local and Global Consistency Using Rich Features,” In Proc. of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014: 298-309.
[J] Yonglei Xiao, Shenghua Liu, Yue Liu, Xue-qi Cheng, Wenjing Zhao, Ren Yan,Yuping Wang, “Semantic Concept Linking and Extension for Social Media Short Texts,” Journal of Chinese Information Processing, Vol. 28, No. 4, July 15th 2014, pp 21-28. (in Chinese)

2013

[C] Shenghua Liu, Fuxin Li, Fangtao Li, Xueqi Cheng and Huawei Shen, “Adaptive Co-Training SVM for Sentiment Classification on Tweets,” In Proc. of ACM International Conference on Information and Knowledge Management (CIKM), 2013: 2079-2088.
[C] Shenghua Liu, Wenjun Zhu, Ning Xu, Fangtao Li, Xueqi Cheng, Yue Liu and Yuanzhuo Wang, “Co-training and Visualizing Sentiment Evolvement for Tweet Events,” In Proc. of the 22nd international conference on World Wide Web (WWW) (Companion Volume), 2013: 105-106.
[C] Xiaohui Yan, Jiafeng Guo, Shenghua Liu, Xueqi Cheng, “Learning Topics in Short Texts by Non-negative Matrix Factorization on Term Correlation Matrix”, In Proc. of the SIAM International Conference on Data Mining (SDM), 2013: 749-757.
[C] Shenghua Liu, Yatao Zhu, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng, and Yue Liu, “A Blending Method for Automated Social Tagging“ In Proc. of Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on. IEEE, 2013, 1: 115-120.

2012

[C] Xiaohui Yan, Jiafeng Guo, Shenghua Liu, Xueqi Cheng, Yanfeng Wang: Clustering short text using Ncut-weighted non-negative matrix factorization. In Proc. of the 21st ACM international conference on Information and knowledge management. ACM (CIKM) Short, 2012: 2259-2262.
[C] Ya-Tao Zhu, Shenghua Liu, Xueqi Cheng, Yue Liu, Yuanzhuo Wang, Jin-Gang Liu: Using WordNet-Based Neighborhood for Improving Social Tag Recommendation. Intelligent Computing Theories and Applications. Springer Berlin Heidelberg, 2012: 221-228.
[C] Peng Cao, Shenghua Liu, Jinhua Gao, Huawei Shen, Jingyuan Li, Yue Liu, Xueqi Cheng: Finding Topic-Related Tweets Using Conversational Thread. Intelligent Information Processing VI. Springer Berlin Heidelberg, 2012. 258-267.
[C] Bolong Zhu, Jinghua Gao, Xiao Han, Cunhui Shi, Shenghua Liu, Yue Liu, Xueqi Cheng: ICTNET at Microblog Track TREC 2012. The Twenty-First Text REtrieval Conference (TREC 2012) Proceedings. 2012.
[J] Yi Mo, Shenghua Liu, Yue Liu, Xueqi Cheng, "An Entropy-Based Rule Mining algorithm for Filtering Tweets on Topics," Journal of Chinese Information Processing, Vol. 26, No. 5, May 2012, pp.1-6. (in Chinese)
[C] Yonglei Xiao, Shenghua Liu, Yue Liu, Xue-qi Cheng, Wenjing Zhao, Ren Yan,Yuping Wang, "Adding Semantic Concepts to Tweets Based on Wikipedia," In Proc. of China Conference on Information Retrieval (CCIR), 2012. (in Chinese)

2011

[C] Peng Cao, Jinhua Gao, Yubao Yu, Shenghua Liu, Yue Liu, Xueqi Cheng: ICTNET at Microblog Track TREC 2011. The Twentieth Text REtrieval Conference (TREC 2011) Proceedings. 2011.
[C] Yi Mo, Shenghua Liu, Yue Liu, Xueqi Cheng, "Push Tweets on Topics: A Rule-Entropy-Based Classifier," In Proc. of China Conference on Information Retrieval (CCIR), 2011. (in Chinese)

until 2010

[J] Shenghua Liu, Guoqiang Chen, Tom Tong Jing, Lei He, Robi Dutta, Xian-Long Hong, "Effective Congestion Reduction for IC Package Substrate Routing," ACM Trans. on Design Automation of Electronic System, Vol. 15, Issue 3, 2010.
[C] Shenghua Liu, Yuchun Ma, Xian-Long Hong, Yu Wang, "Simultaneous Slack Budgeting and Retiming for Synchronous Circuits Optimization", In Proc. of 15th Asia and South Pacific Design Automation Conference (ASP-DAC), 2010, pp. 49-54. (Best paper candidate)
[J] Shenghua Liu, Guoqiang Chen, Tom Tong Jing, Lei He, Tianpei Zhang, Robi Dutta, Xian-Long Hong, "Substrate Topological Routing for High Density Packages," IEEE trans. on Computer Aided Design, Vol.28, No.2, 2009, pp207-216.
[C] Shenghua Liu, Guoqiang Chen, Tom Tong Jing, Lei He, Robi Dutta, Xian-Long Hong, "Diffusion-Driven Congestion Reduction for Substrate Topological Routing," In Proc. of International Symposium on Physical Design (ISPD SIGDA) 2009, Mar. 2009, pp175-180. [ slices ]
[C] Shenghua Liu, Guoqiang Chen, Tom Tong Jing, Lei He, Tianpei Zhang, Robi Dutta, Xian-Long Hong, "Topological Routing to Maximize Routability for Package Substrate," In Proc. of 45th ACM/EDAC/IEEE Design Automation Conference (DAC), Anaheim, June 2008, pp.566-569.
[J] Shenghua Liu, Xianlong Hong, Tong Jing, Jingyu Xu, "FREe: A Fast Routability Estimator," In Proc. of IEEE International Conference on Communications, Circuits and Systems, ICCCAS 2006: 2454-2458.
[C] Fubing Mao, Yuchun Ma, Ning Xu, Shenghua Liu, Yu Wang, Xianlong Hong, "Congestion-driven floorplanning based on two-stage optimization," In Proc. of IEEE 8th International Conference on ASIC, ASICON 2009: 1298- 1301.

[Patents]

DYNAMIC PUSH FOR TOPOLOGICAL ROUTING OF SEMICONDUCTOR PACKAGES, filing date: June 6, 2008, patent number: 8006216, issue date: Aug. 23, 2011, Guoqiang Chen, Kaushik Sheth, Egino Sarto, and Shenghua Liu.(US patent, issued)
A FAST ROUTABILITY ESTIMATING METHOD IN VLSI, filing date 6/15/2006 (ZL 200610012271.4), issued date 7/23/2008 (CN 100405379C), by Xianlong Hong, Tong Jing, Shenghua Liu, and Jingyu Xu. (Chinese patent, issued)
AN AUTOMATED SOCIAL TAGGING METHOD AND SOCIAL TAG GENERATOR, filing date: 9/7/2011, patent number 201110263798.5, Shenghua Liu, Xueqi Cheng, Jiafeng Guo, Yue Liu, Huaming Liao, and Yatao Zhu.(Chinese patent, filed)
AN OPEN-KNOWLEDGE-BASED SEMANTIC CONCEPT LINKING AND EXTENSION METHOD FOR SHORT TEXT, filing date: 3/15/2013, patent number 201310081984.6, issue date: 4/1/2015, issue number: CN 103150382B, Xueqi Cheng, Shenghua Liu, Yonglei Xiao, Yuanzhuo Wang, and Yue Liu. (Chinese patent, issued)
AN EVENT EVOLVEMENT ANALYSING METHOD FOR SHORT TEXT, filing date: 3/15/2013, patent number: ZL 201310082990.3, Xueqi Cheng, Shenghua Liu, Fuxin Li, Yuanzhuo Wang, and Yue Liu. (Chinese patent, issued)
A VISUALIZING METHOD FOR DYNAMIC OPINIONS IN SOCIAL MEDIA, file date: 4/18/2013, patent number: ZL 201310134433.1, Xueqi Cheng, Shenghua Liu, Yatao Zhu, Yuanzhuo Wang, Yue Liu, and Wenjun Zhu. (Chinese patent, issued)
A REALTIME EVENT FILTERING METHOD AND SYSTEM FOR STREAMING WEB DATA, filing date: 4/19/2013, patent number: ZL 201310136896.1, Xueqi Cheng, Shenghua Liu, Wenyi Qiu, Yuanzhuo Wang, Yue Liu, Yi Mo, and Zhankun Huang. (Chinese patent, issued)

spartan2: a developing open-sourced graph and time series mining package based on sparse tensor/matrix and sequential analysis. [git repository]
HoloScope:Topology-and-Spike Aware Fraud Detection in big graph [ code ]
SpecGreedy: Unified Dense Subgraph Detection [code]
Heatmap for point list [ download ]
NeuCast for time series forecasting in power grid [download]
BeatGAN: Anomaly detection in time series, e.g. ECG, and sensor data of motions. [ code ]
EagleMine: vision-guided anomaly detection in large graphs [ code ]
FlowScope: fast algorithm for multipartite subgraph detection, and used for spotting money laundering [ code ]
EigenPulse: detect anomalies in streaming graphs [code]
CatchCore: detecting dense blocks which show hierachical structures and have a core. [ code ]

Disseration demo

Research on Topological Routing Algorithms on System in Package (SiP) [ demo on chip package , demo on SiP package ]

Students under supervision

Yuyao Ge (Ph.D. candidate, advising on large language models and graphs, 2023 (senior))
Xuanshan Zhou (Master candidate, advising on scalable graph summarization, 2023 (senior))
Baolong Bi (Ph.D. student, advising on foundation model and graphs, 2022(senior), 2023.9- )
Tianjie Hou (Master student, advising on graph anomaly detection, 2022(senior), 2023.9- )
Li Wang (Master student, advising on temporal graph mining, 2021(senior), 2022.9- )
Lingrui Mei (Ph.D. student, advising on reliable large language models 2024.10- ; co-advised Master student (2022) 2023.9-2024.10)

Alumni

Xiaobing Sun, Master, advised on anomaly detection and graph learning, 2020(senior), 2021.9-2024.7, now at ByteDance, Beijing.
Houquan Zhou, Ph.D., co-advised on big graph summarization and representation learning, 2018.9-2024.7, now at Tencent Game, Shenzhen.
Yugao Zhu: advised on big graph mining algorithm and spectral graph theory 2021-2023, transferred to HKUST since 2024
Caizheng Liu: Ph.D., co-advised on time series mining, 2017 - 2023.6, now a post-doc
Quan Ding: Master, advised on time series mining and anomaly detection, 2019(senior), 2020.9-2023.6, now at a startup, Shenzhen.
Siwei Zeng: Master, advised on big graph mining, 2018(senior), 2019.9-2022.6, now at ByteDance, Beijiing.
Bowei Lin: Master, co-advised on graph matching, 2019.9-2022.6, now a R&D Engineer at ICT, Beijing
Jiabao Zhang: Master, advised on streaming graph and tensor mining, 2017(senior), 2018.9-2021.6, now at Didi research group, Beijing
Wenjie Feng : co-advised Ph.D. student, big graph mining, 2016 - 2020.9, now Post-doc at NUS, Singapore.
Bin Zhou: Master, advised on time series mining, 2016 (senior), 2017.9-2020.6, now at Meituan.
Haiyin Zhang: visiting undergraduate, 2019.4-2020.6, now Master student at TU Delft, Dutch.
Xin Zhao (undergraduate, co-advised on big sparse tensor computing system, 2018(senior) )
Xiangfeng Li (co-advised graduate student, visiting, 2018.5-2020.6)
Xiaotong Jiang (visiting undergraduate, 2018.7-2019.7, now Master student at USC, US )
Pudi Chen (co-advised graduate student, visiting, 2017.10-2019.6, now at Ant financial services group)
Yang Liu (visiting Master, 2016-2017)
Qi Cao (co-advised Master, now Ph.D. at ICT CAS, 2017-)
Yongqing Wang (co-advised Ph.D., 2015-2017, now associate professor at ICT)
Tong Man (co-advised Ph.D., now at Amozon U.S.)
Yonglei Xiao (Master, now at Sohu)
Houdong Zheng (visiting Master, Fall 2015 - Fall 2016)
Yatao Zhu (Master, now ph.D.)
Yi Mo (Master, now at Tencent)
Zhankun Huang (Master, now at Netease)
Peng Cao (co-advised Ph.D. now overseas)
Fuxin Li (Master, now at MI Technology)
Wenyi Qiu (visiting undergraduate from CUG, now at ZhongRuan Inc)
Bin Wang (visiting Master from WHUT)
Wenjun Zhu (visiting Master from WHUT)
Xiaoli Wang (visiting Master from Xidian Univ.)
Wenjing Zhao (visiting Master from Xidian Univ.)