# Student supervised; ^ Research staff supervised; * Corresponding author (journal articles)

Refereed Conference Papers

1. Non-Homophilic Graph Pre-Training and Prompt Learning.
   Xingtong Yu^, Jie Zhang, Yuan Fang and Renhe Jiang. Accepted by KDD 2025.
   [Paper] [Code]

2. Learning to Identify Seen, Unseen and Unknown in the Open World: A Practical Setting for Zero-Shot Learning.
   Sethupathy Parameswaran^, Yuan Fang, Chandan Gautam, Savitha Ramasamy and Xiaoli Li. Accepted by WACV 2025.
   [Paper] [Code]

3. A Contrastive Framework with User, Item and Review Alignment for Recommendation.
   Viet Hoang Dong#, Yuan Fang and Hady W. Lauw. Accepted by WSDM 2025.
   [Paper] [Code]

4. An Aspect Performance-Aware Hypergraph Neural Network for Review-based Recommendation.
   Junrui Liu#, Tong Li, Wu Di, Zifang Tang and Yuan Fang, Zhen Yang. Accepted by WSDM 2025.
   [Paper] [Code]

5. Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs.
   Ran Liu#, Zhongzhou Liu#, Xiaoli Li and Yuan Fang. In EMNLP 2024, pp. 17525--17537.
   [Paper] [Poster] [Slides] [Code]

6. A Survey of Ontology Expansion for Conversational Understanding.
   Jinggui Liang, Yuxia Wu^, Yuan Fang, Hao Fei and Lizi Liao. In EMNLP 2024, pp. 18111--18127.
   [Paper] [Code]

7. Class Name Guided Out-of-Scope Intent Classification.
   Chandan Gautam, Sethupathy Parameswaran^, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Kumar Sahu and Xiaoli Li. In EMNLP Findings 2024, pp.  9100--9112.
   [Paper] [Code]

8. Collaborative Cross-modal Fusion with Large Language Model for Recommendation.
   Zhongzhou Liu#, Hao Zhang, Kuicai Dong and Yuan Fang. In CIKM 2024, pp. 1565--1574.
   [Paper] [Slides] [Data]

9. A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs.
   Amitoz Azad^ and Yuan Fang. In KDD 2024,  pp. 49--58.
   [Paper] [Slides] [Code]

10. SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning.
    Zhihao Wen^, Jie Zhang and Yuan Fang. In ACL Findings 2024, pp. 1241--1257.
    [Paper] [Code]

11. Contrastive General Graph Matching with Adaptive Augmentation Sampling.
    Jianyuan Bo# and Yuan Fang. In IJCAI 2024, pp. 3724--3732.
    [Paper] [Supplementary] [Slides] [Poster] [Code]

12. Heterogeneous Graph Transformer with Poly-Tokenization.
    Zhiyuan Lu, Yuan Fang, Cheng Yang and Chuan Shi. In IJCAI 2024, pp. 2234--2242.
    [Paper] [Slides] [Poster] [Code]

13. MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs.
    Xingtong Yu#, Chang Zhou, Yuan Fang and Xinming Zhang. In TheWebConf 2024, pp. 515--526.
    [Paper] [Poster] [Code]

14. On the Feasibility of Simple Transformer for Dynamic Graph Modeling.
    Yuxia Wu^, Yuan Fang and Lizi Liao. In TheWebConf 2024, pp. 870--880.
    [Paper] [Poster] [Code]

15. Diffusion-based Negative Sampling on Graphs for Link Prediction.
    Trung-Kien Nguyen^, Yuan Fang. In TheWebConf 2024, pp. 948--958.
    [Paper] [Slides] [Poster] [Code]

16. HGPrompt: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning.
    Xingtong Yu#, Yuan Fang, Zemin Liu and Xinming Zhang. In AAAI 2024, pp. 16578--16586.
    [Paper] [Code] [Poster]

17. Estimating Propensity for Causality-based Recommendation without Exposure Data.
    Zhongzhou Liu#, Yuan Fang and Min Wu. In NeurIPS 2023.
    [Paper] [Code] [Poster] [中文概述]

18. Graph Contrastive Learning with Stable and Scalable Spectral Encoding.
    Deyu Bo#, Yuan Fang, Yang Liu, Chuan Shi. In NeurIPS 2023.
    [Paper] [Code] [Poster]

19. Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks.
    Zhihao Wen#, Yuan Fang, Yihan Liu, Yang Guo and Shuji Hao. In CIKM 2023 (Applied Research), pp.  4864--4870.
    [Paper] [Code] [Slides] [中文概述]

20. Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting.
    Zhihao Wen# and Yuan Fang. In SIGIR 2023, pp. 506--516.
    [Paper] [Code] [Slides] [中文概述]

21. Link Prediction on Latent Heterogeneous Graphs.
    Trung-Kien Nguyen^, Zemin Liu^ and Yuan Fang. In TheWebConf 2023, pp. 263--273.
    [Paper] [Code] [Slides]

22. GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks.
    Zemin Liu^, Xingtong Yu#, Yuan Fang and Xinming Zhang. In TheWebConf 2023,  pp. 417--428.
    [Paper] [Code] [Slides] [中文概述]

23. On Generalized Degree Fairness in Graph Neural Networks.
    Zemin Liu^, Trung-Kien Nguyen^ and Yuan Fang. In AAAI 2023, pp.  4525--4533.
    [Paper] [Supplementary] [Code] [Slides] [Poster]

24. Learning to Count Isomorphisms with Graph Neural Networks.
    Xingtong Yu#, Zemin Liu^, Yuan Fang and Xinming Zhang. In AAAI 2023, pp.  4845--4853.
    [Paper] [Supplementary] [Code] [Slides] [Poster] [中文概述]

25. End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection.
    Tiancheng Huang, Donglin Wang, Yuan Fang and Zhengyu Chen. In IJCAI 2022, pp. 2087--2093.
    [Paper] [Slides] [Poster]

26. TREND: TempoRal Event and Node Dynamics for Graph Representation Learning.
    Zhihao Wen# and Yuan Fang. In TheWebConf 2022, pp. 1159--1169.
    [Paper] [Code] [Slides] [中文概述]

27. On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks.
    Zemin Liu^, Qiheng Mao, Chenghao Liu, Yuan Fang and Jianling Sun. In TheWebConf 2022, 1506--1516.
    [Paper] [Code]

28. Contrastive Pre-training of GNNs on Heterogeneous Graphs.
    Xunqiang Jiang, Yuanfu Lu#, Yuan Fang and Chuan Shi. In CIKM 2021, pp. 803--812.
    [Paper] [Code] [Slides]

29. Topic-aware Heterogeneous Graph Neural Network for Link Prediction.
    Siyong Xu, Cheng Yang, Chuan Shi, Yuan Fang, Yuxin Guo, Tianchi Yang, Luhao Zhang and Maodi Hu. In CIKM 2021, pp. 2261--2270.
    [Paper] [Code] [Slides]

30. Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process.
    Yugang Ji#, Tianrui Jia, Yuan Fang and Chuan Shi. In ECML-PKDD 2021 Part I, pp. 388--403.
    [Paper] [Code] [Slides]

31. Tail-GNN: Tail-Node Graph Neural Networks.
    Zemin Liu^, Trung-Kien Nguyen^ and Yuan Fang. In KDD 2021, pp. 1109--1119.
    [Paper] [Code] [Slides] [Poster] [中文概述]

32. Pre-training on Large-Scale Heterogeneous Graph.
    Xunqiang Jiang, Tianrui Jia, Yuan Fang, Chuan Shi, Zhe Lin, Hui Wang. In KDD 2021, pp. 756--766 .
    [Paper] [Code] [Slides] [Poster]

33. Node-wise Localization of Graph Neural Networks.
    Zemin Liu^, Yuan Fang, Chenghao Liu^ and Steven C. H. Hoi. In IJCAI 2021, pp. 1520--1526.
    [Paper] [Supplementary] [Code] [Slides] [Poster]

34. Meta-Inductive Node Classification across Graphs.
    Zhihao Wen#, Yuan Fang and Zemin Liu^. In SIGIR 2021, pp. 1219--1228.
    [Paper] [Code] [Slides]

35. Learning to Pre-train Graph Neural Networks.
    Yuanfu Lu#, Xunqiang Jiang, Yuan Fang and Chuan Shi. In AAAI 2021, pp. 4276--4284.
    [Paper] [Supplementary] [Code] [Slides] [Poster] [中文概述]

36. Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph.
    Zemin Liu^, Yuan Fang, Chenghao Liu^ and Steven C. H. Hoi. In AAAI 2021, pp. 4267--4275.
    [Paper] [Supplementary] [Code] [Slides] [Poster]

37. Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks.
    Zemin Liu^, Wentao Zhang#, Yuan Fang, Xinming Zhang and Steven C. H. Hoi. In CIKM 2020, pp. 975--984.
    [Paper] [Code] [Slides]

38. TPR: Text-aware Preference Ranking for Recommender Systems.
    Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang and Ee-Peng Lim. In CIKM 2020, pp. 215--224.
    [Paper] [Code] [Slides]

39. Adaptive Task Sampling for Meta-Learning.
    Chenghao Liu^, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang and Steven C. H. Hoi. In ECCV 2020 Part XVIII, pp 752--769.
    [Paper] [Supplementary] [Code] [Slides]

40. Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation.
    Yugang Ji#, Mingyang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia and Chuan Shi. In ECML-PKDD 2020 Part III, pp. 314--329.
    [Paper] [Code] [Slides]

41. Social Influence Attentive Neural Network for Friend-Enhanced Recommendation.
    Yuanfu Lu#, Ruobing Xie, Chuan Shi, Yuan Fang, Wei Wang, Xu Zhang and Leyu Lin. In ECML-PKDD 2020 Part IV (Applied Data Science), pp. 3--18.
    [Paper] [Code] [Slides] [中文概述] 

42. Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation.
    Yuanfu Lu#, Yuan Fang and Chuan Shi. In KDD 2020, pp. 1563--1573.
    [Paper] [Code] [Slides] [Video] [Poster] [中文概述] 

43. BiANE: Bipartite Attributed Network Embedding.
    Wentao Huang, Yuchen Li, Yuan Fang, Ju Fan and Hongxia Yang. In SIGIR 2020, pp. 149--158.
    [Paper] [Code] [Slides] [中文概述] 

44. Multiplex Memory Network for Collaborative Filtering.
    Xunqiang Jiang, Binin Hu#, Yuan Fang and Chuan Shi. In SDM 2020, pp. 91--99.
    [Paper] [Supplementary] [Code] [中文概述] 

45. Correlation-Sensitive Next-Basket Recommendation.
    Duc-Trong Le, Hady W. Lauw and Yuan Fang. In IJCAI 2019, pp. 2808--2814.
    [Paper] [Code] [Slides] [Poster] 

46. Adversarial Learning on Heterogeneous Information Networks.
    Binbin Hu#, Yuan Fang and Chuan Shi. In KDD 2019, pp. 120--129.
    [Paper] [Code] [Poster] [中文概述] 

47. Heterogeneous Embedding Propagation for Large-scale E-Commerce User Alignment.
    Vincent W. Zheng, Mo Sha, Yuchen Li, Hongxia Yang, Yuan Fang, Zhenjie Zhang, Kian-Lee Tan and Kevin C.-C. Chang. In ICDM 2018 (Short), pp. 1434--1439.
    [Paper] [Slides] [Poster]

48. Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation.
    Duc-Trong Le, Hady W. Lauw and Yuan Fang. In IJCAI 2018, pp. 3414--3420.
    [Paper] [Code] [Slides] [Poster]

49. Region Average Pooling for Context-Aware Object Detection.
    Kingsley Kuan, Gaurav Manek, Jie Lin, Yuan Fang and Vijay Chandrasekhar. In ICIP 2017, pp. 1347--1351.
    [Paper]

50. Object Detection Meets Knowledge Graphs.
    Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan and Vijay Chandrasekhar. In IJCAI 2017, pp. 1661--1667.
    [Paper] [Code] [Slides] [Poster]

51. Basket-Sensitive Personalized Item Recommendation.
    Duc-Trong Le,  Hady W. Lauw and Yuan Fang. In IJCAI 2017, pp. 2060--2066.
    [Paper] [Slides] [Poster]

52. Modeling Sequential Preferences with Dynamic User and Context Factors.
    Duc-Trong Le, Yuan Fang and Hady W. Lauw. In ECML-PKDD 2016, pp. 145--161.
    [Paper] [Supplementary] [Slides] [Poster]

53. Learning to Query: Focused Web Page Harvesting for Entity Aspects.
    Yuan Fang, Vincent W. Zheng and Kevin C.-C. Chang. In ICDE 2016, pp. 1002--1013.
    [Paper] [Slides] [Poster]

54. Semantic Proximity Search on Graphs with Metagraph-based Learning.
    Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Kevin C.-C. Chang and Xiao-Li Li. In ICDE 2016, pp. 277--288.
    [Paper] [Code] [Slides] [Poster]

55. Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically.
    Yuan Fang, Kevin C.-C. Chang and Hady W. Lauw. In ICML 2014 (2), pp. 406--414.
    [Paper] [Supplementary] [Data] [Slides] 

56. Incremental and Accuracy-Aware Personalized PageRank through Scheduled Approximation.
    Fanwei Zhu, Yuan Fang, Kevin C.-C. Chang and Jing Ying. In VLDB 2013, 6(6), pp. 481--492.
    Extended version invited to the collection of Best Papers of VLDB'13.
    [Paper] [Data] [Slides] 

57. RoundTripRank: Graph-based Proximity with Importance and Specificity.
    Yuan Fang, Kevin C.-C. Chang and Hady W. Lauw. In ICDE 2013, pp. 613--624.
    [Paper] [Data] [Slides] [Poster] 

58. Confidence-Aware Graph Regularization with Heterogeneous Pairwise Features.
    Yuan Fang, Bo-June P. Hsu and Kevin C.-C. Chang. In SIGIR 2012, pp. 951--960.
    [Paper] [Slides] 

59. Searching Patterns for Relation Extraction over the Web: Rediscovering the Pattern-Relation Duality.
    Yuan Fang and Kevin C.-C. Chang. In WSDM 2011, pp. 825--834.
    [Paper] [Poster] [Notes]

60. Privacy beyond Single Sensitive Attribute.
    Yuan Fang, Mafruz Zaman Ashrafi and See-Kiong Ng. In DEXA 2011, pp. 187--201.
    [PDF] 

61. Efficient Skyline Maintenance for Streaming Data with Partially-Ordered Domains.
    Yuan Fang and Chee-Yong Chan. In DASFAA 2010, pp. 322--336.
    [Paper] [Slides]

Refereed Journal Papers

1. Temporal Relational Graph Convolutional Network Approach to Financial Performance Prediction.
   Brindha Priyadarshini Jeyaraman#, Bing Tian Dai, Yuan Fang. In MAKE 6(4), pp. 2303--2320.
   [PDF]

2. An end-to-end bi-objective approach to deep graph partitioning.
   Pengcheng Wei^, Yuan Fang, Zhihao Wen, Zheng Xiao, Binbin Chen. In Neural Networks 181, 2024, Article No. 106823.
   [PDF]

3. Prompt Tuning on Graph-augmented Low-resource Text Classification.
   Zhihao Wen# and Yuan Fang. In IEEE TKDE 36(12), 2024, pp. 9080--9095.
   [PDF] [Code]

4. Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs.
   Xingtong Yu#, Zhenghao Liu, Yuan Fang*, Zemin Liu, Sihong Chen and Xinming Zhang*. In IEEE TKDE 36(11), 2024, pp. 6237--6250.
   [PDF] [Code]

5. Dynamic Meta-path Guided Temporal Heterogeneous Graph Neural Networks.
   Yugang Ji, Chuan Shi* and Yuan Fang.  In World Scientific Annual Review of Artificial Intelligence 1, 2024, Article No. 2350002.
   [Paper]

6. Locality-Aware Tail Node Embeddings on Homogeneous and Heterogeneous Networks.
   Zemin Liu^, Yuan Fang*, Wentao Zhang#, Xinming Zhang* and Steven C. H. Hoi. In IEEE TKDE 36(6), 2023, pp. 2517--2532.
   [Paper] [Code]

7. Motif Graph Neural Network.
   Xuexin Chen, Ruichu Cai*, Yuan Fang*, Min Wu*, Zijian Li and Zhifeng Hao. In IEEE TNNLS 35(10), 2023, pp. 14833--14847.
   [Paper] [Supplementary] [Code]

8. Dual-View Preference Learning for Adaptive Recommendation.
   Zhongzhou Liu#, Yuan Fang* and Min Wu*. In IEEE TKDE 35(11), 2023, pp. 11316--11327.
   [Paper] [Code]

9. Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation.
   Zhongzhou Liu#, Yuan Fang* and Min Wu*. In ACM TOIS 41(3), Article No. 55, 2023.
   Invited for oral presentation at SIGIR'23.
   [Paper] [Code] [Slides] [中文概述]

10. Pre-training Graph Neural Networks for Link Prediction in Biomedical Networks.
    Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Keong Kwoh, Jinmiao Chen, Jiawei Luo and Xiao-Li Li. In Bioinformatics 38(8), 2022, pp. 2254--2262.
    [Paper] [Supplementary] [Code] 

11. Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle.
    Fanwei Zhu, Yuan Fang*, Kai Zhang, Kevin C.-C. Chang, Hongtai Cao, Zhen Jiang and Minghui Wu. In IEEE TKDE 35(3), 2021, pp. 3195--3210.
    [Paper] [Code] 

12. Neighbor-Anchoring Adversarial Graph Neural Networks.
    Zemin Liu^, Yuan Fang*, Yong Liu, Vincent W. Zheng. In IEEE TKDE 35(1), 2021, pp. 784--795. 
    [Paper] [Code] 

13. Prediction of Synthetic Lethal Interactions in Human Cancers using Multi-view Graph Auto-Encoder.
    Zhifeng Hao, Di Wu, Yuan Fang*, Min Wu*, Ruichu Cai* and Xiao-Li Li. In IEEE J. of Biomedical & Health Informatics (JBHI) 25(10), 2021, pp.  4041--4051.
    [Paper] [Supplementary] [Code]

14. Multi-View Collaborative Network Embedding.
    Sezin Kircali Ata, Yuan Fang*, Min Wu*, Jiaqi Shi^, Chee Keong Kwoh, Xiao-Li Li. In ACM TKDD 15(3), Article No. 39, 2021.
    [Paper] [Code] 

15. Recent Advances in Network-based Methods for Disease Gene Prediction.
    Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh and Xiao-Li Li*. In Briefings in Bioinformatics 22(4), 2020, Article No. bbaa303.
    [Paper] [Code] 

16. Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling.
    Yugang Ji#, Mingyang Yin, Hongxia Yang, Jingren Zhou, Vincent W. Zheng, Chuan Shi* and Yuan Fang*. In ACM TKDD 15(1), 2020, Article No.10.
    [Paper] [Code]

17. Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks.
    Yugang Ji#, Chuan Shi*, Yuan Fang, Xiangnan Kong and Mingyang Yin. In IPM 57(6), 2020.
    [Paper] [Code] 

18. mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding.
    Wentao Zhang#, Yuan Fang*, Zemin Liu^, Min Wu and Xinming Zhang*. In IEEE TKDE 34(3), 2020, pp. 1317--1329.
    [Paper] [Code] 

19. Dual-Dropout Graph Convolutional Network for Predicting Synthetic Lethality in Human Cancers.
    Ruichu Cai, Xuexin Chen#, Yuan Fang*, Min Wu* and Yuexing Hao. In Bioinformatics 36(16), 2020, pp. 4458--4465.
    [Paper] [Code] [Supplementary] 

20. Metagraph-based Learning on Heterogeneous Graphs.
    Yuan Fang, Wenqing Lin, Vincent W. Zheng*, Min Wu, Jiaqi Shi^, Kevin C.-C. Chang and Xiao-Li Li. In IEEE TKDE 33(1), 2019, pp. 154--168.
    [Paper] [Code]

21. Integrating Node Embeddings and Biological Annotations for Genes to Predict Disease-Gene Associations.
    Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu* and Xiao-Li Li. In BMC Systems Biology 12(Supp 9), 2018, pp. 31--44.
    Invited for oral presentation at GIW'18.
    [Paper]

22. Disease Gene Classification with Metagraph Representations.
    Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li* and Xiaokui Xiao. In Methods 131, 2017, pp. 83--92.
    [Paper] 

23. Scheduled Approximation for Personalized PageRank with Utility-Driven Hub Selection.
    Fanwei Zhu*, Yuan Fang, Kevin C.-C. Chang and Jing Ying. In VLDBJ 24(5), 2015, Special Issue on Best Papers of VLDB'13, pp. 655--679.
    [Paper] [Data] 

24. Entity Linking on Microblogs with Spatial and Temporal Signals.
    Yuan Fang and Ming-Wei Chang. In TACL 2(Oct), 2014, pp. 259--272.
    Invited for oral presentation at EMNLP'14. 
    [Paper] [Slides] [Data] 

Editorials, Front Matters

1. Lecture-style Tutorial: Towards Graph Foundation Models.
   Chuan Shi, Cheng Yang, Yuan Fang, Lichao Sun and Philip Yu. In TheWebConf 2024 (Companion), pp. 1264--1267.
   [PDF] [Part I] [Part II] [Part III]

2. The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021).
   Chuan Shi, Yuan Fang , Yanfang Ye , Jiawei Zhang. In KDD 2021, pp. 4157--4158.
   [PDF]

Workshop/Demo Papers, Abstracts 

1. Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle (Extended Abstract).
   Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin C.-C. Chang, Hongtai Cao, Zhen Jiang, Minghui Wu. In ICDE 2022 (TKDE Posters), pp. 1569--1570.
   [Extended abstract] [Poster]

2. Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract).
   Zemin Liu^, Yuan Fang, Yong Liu, Vincent W. Zheng. In ICDE 2022 (TKDE Posters), pp. 1571--1572.
   [Extended abstract] [Poster]

3. Network Embedding with Attribute Refinement. [Workshop paper]
   Tong Xiao#, Yuan Fang, Hongxia Yang and Vincent W. Zheng. In KDD Workshop on Mining and Learning with Graphs 2017 (non-archival).
   [Paper] [Video]

4. Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text. [Workshop paper]
   Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar. In CVPR Workshop on YouTube-8M Challenge 2017 (non-archival).
   Ranked 22/655 in the YouTube-8M Kaggle challenge.
   [Paper] 

5. ARISE-PIE: A People Information Integration Engine over the Web. [Workshop paper]
   Vincent W. Zheng, Tao Hoang, Penghe Chen, Yuan Fang, Xiaoyan Yang and Kevin C.-C. Chang. In CIKM Workshop on Data-Driven Talent Acquisition 2016 (non-archival).
   [Paper] [Demo] 

6. IntelligShop: Enabling Intelligent Shopping in Malls through Location-based Augmented Reality. [Demo paper]
   Aditi Adhikari, Vincent W. Zheng, Hong Cao, Miao Lin, Yuan Fang and Kevin C.-C. Chang. In ICDM Workshops 2015, pp. 1604--1607.
   Featured in media coverage in the United States.
   [Paper] [Demo] [Media Coverage]  

7. Differences in Plasma Lipids during Rested Wakefulness and Sleep Deprivation. [Abstract only]
   Sha Huang, Eric Chern-Pin Chua, Guanghou Shui, Yuan Fang, Sing-Chen Yeo, Markus Wenk and Joshua J. Gooley. In SLEEP 2015.
   [Abstract] [Poster] 

Book Chapters 

1. Disease Gene Classification with Metagraph Representations.
   Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li and Xiaokui Xiao. In Data Mining for Systems Biology: Methods and Protocols, 2nd Edition, 2018, pp. 211--224. Humana Press, New York, NY.
   [Link] 

Preprints 

1. Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction.
   Zhihao Wen^, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu.
   [PDF]

2. A Survey of Few-Shot Learning on Graphs: from Meta-learning to Pre-training and Prompting.
   Xingtong Yu#, Yuan Fang, Zemin Liu, Yuxia Wu, Zhihao Wen, Jianyuan Bo, Xinming Zhang and Steven C.H. Hoi.
   [PDF] [Resource]

3. Towards Graph Foundation Models: A Survey and Beyond.
   Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu and Chuan Shi.
   [PDF] [Resource]

4. On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach.
   Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao and Zhifeng Hao.
   [PDF]

5. A Survey on Spectral Graph Neural Networks.
   Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li and Chuan Shi.
   [PDF]

6. End-to-End Video Classification with Knowledge Graphs.
   Yuan Fang, Zhe Wang, Jie Lin, Luis Fernando D’Haro, Kim Jung Jae, Zeng Zeng and Vijay Chandrasekhar.
   [PDF] 

Dissertations 

1. Walking Forward and Backward: Towards Graph-based Searching and Mining.
   Yuan Fang. PhD Dissertation, University of Illinois at Urbana-Champaign, 2014.
   [PDF] 

2. Finding Skyline Objects in Streaming Data.
   Yuan Fang. Honors Year Dissertation, National University of Singapore, 2009.
   [PDF]