[Google Scholar] [ORCID] [ResearchGate] [Semantic Scholar]
Refereed Conference
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting.
Z. Wen and Y. Fang. Accepted by SIGIR 2023.
[Paper] [Code] [Slides] [Poster]Link Prediction on Latent Heterogeneous Graphs.
K. Nguyen, Z. Liu and Y. Fang. Accepted by TheWebConf 2023.
[Paper] [Code] [Slides] [Poster]GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks.
Z. Liu*, X. Yu*, Y. Fang and X. Zhang. Accepted by TheWebConf 2023.
(* Co-first authors with equal contribution.)
[Paper] [Code] [Slides] [Poster]On Generalized Degree Fairness in Graph Neural Networks.
Z. Liu, K. Nguyen and Y. Fang. Accepted by AAAI 2023.
[Paper] [Code] [Slides] [Poster]Learning to Count Isomorphisms with Graph Neural Networks.
X. Yu*, Z. Liu*, Y. Fang and X. Zhang. Accepted by AAAI 2023.
(* Co-first authors with equal contribution.)
[Paper] [Code] [Slides] [Poster]End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection.
T. Huang, D. Wang, Y. Fang and Z. Chen. In IJCAI 2022, pp. 2087--2093.
[Paper] [Code] [Slides] [Poster]TREND: TempoRal Event and Node Dynamics for Graph Representation Learning.
Z. Wen and Y. Fang. In TheWebConf 2022, pp. 1159--1169.
[Paper] [Code] [Slides]On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks.
Z. Liu, Q. Mao, C. Liu, Y. Fang and J. Sun. In TheWebConf 2022, 1506--1516.
[Paper] [Code] [Slides]Contrastive Pre-training of GNNs on Heterogeneous Graphs.
X. Jiang, Y. Lu, Y. Fang and C. Shi. In CIKM 2021, pp. 803--812.
[Paper] [Code] [Slides]Topic-aware Heterogeneous Graph Neural Network for Link Prediction.
S. Xu, C. Yang, C. Shi, Y. Fang, Y. Guo, T. Yang, L. Zhang and M. Hu. In CIKM 2021, pp. 2261--2270.
[Paper] [Code] [Slides]Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process.
Y. Ji, T. Jia, Y. Fang and C. Shi. In ECML-PKDD 2021 Part I, pp. 388--403.
[Paper] [Code] [Slides]Tail-GNN: Tail-Node Graph Neural Networks.
Z. Liu, K. Nguyen and Y. Fang. In KDD 2021, pp. 1109--1119.
[Paper] [Code] [Slides] [Poster]Pre-training on Large-Scale Heterogeneous Graph.
X. Jiang, T. Jia, C. Shi, Y. Fang, Z. Lin and H. Wang. In KDD 2021, pp. 756--766 .
[Paper] [Code] [Slides] [Poster]Node-wise Localization of Graph Neural Networks.
Z. Liu, Y. Fang, C. Liu and S. C. H. Hoi. In IJCAI 2021, pp. 1520--1526.
[Paper] [Supplementary] [Code] [Slides] [Poster]Meta-Inductive Node Classification across Graphs.
Z. Wen, Y. Fang and Z. Liu. In SIGIR 2021, pp. 1219--1228.
[Paper] [Code] [Slides]Learning to Pre-train Graph Neural Networks.
Y. Lu, X. Jiang, Y. Fang and C. Shi. In AAAI 2021, pp. 4276--4284.
[Paper] [Supplementary] [Code] [Slides] [Poster]Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph.
Z. Liu, Y. Fang, C. Liu and S. C. H. Hoi. In AAAI 2021, pp. 4267--4275 .
[Paper] [Supplementary] [Code] [Slides] [Poster]Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks.
Z. Liu*, W. Zhang*, Y. Fang, X. Zhang and S. C. H. Hoi. In CIKM 2020, pp. 975--984.
(* Co-first authors with equal contribution.)
[Paper] [Code] [Slides]TPR: Text-aware Preference Ranking for Recommender Systems.
Y.-N. Chuang, C.-M. Chen, C.-J. Wang, M.-F. Tsai, Y. Fang and E. P. Lim. In CIKM 2020, pp. 215--224.
[Paper] [Code] [Slides]Adaptive Task Sampling for Meta-Learning.
C. Liu*, Z. Wang*, D. Sahoo, Y. Fang, K. Zhang and S. C. H. Hoi. In ECCV 2020 Part XVIII, pp 752--769.
(* Co-first authors with equal contribution.)
[Paper] [Supplementary] [Code] [Slides]Temporal Heterogeneous Interaction Graph Embedding For Next-Item Recommendation.
Y. Ji, M. Yin, Y. Fang, H. Yang, X. Wang, T. Jia and C. Shi. In ECML-PKDD 2020 Part III, pp. 314--329.
[Paper] [Code] [Slides]Social Influence Attentive Neural Network for Friend-Enhanced Recommendation.
Y. Lu, R. Xie, C. Shi, Y. Fang, W. Wang, X. Zhang and L. Lin. In ECML-PKDD 2020 Part IV (Applied Data Science), pp. 3--18.
[Paper] [Code] [Slides] [中文概述]Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation.
Y. Lu, Y. Fang and C. Shi. In KDD 2020, pp. 1563--1573.
[Paper] [Code] [Slides] [Video] [Poster] [中文概述]BiANE: Bipartite Attributed Network Embedding.
W. Huang, Y. Li, Y. Fang, J. Fan and H. Yang. In SIGIR 2020, pp. 149--158.
[Paper] [Code] [Slides] [中文概述]Multiplex Memory Network for Collaborative Filtering.
X. Jiang, B. Hu, Y. Fang and C. Shi. In SDM 2020, pp. 91--99.
[Paper] [Supplementary] [Code] [中文概述]Correlation-Sensitive Next-Basket Recommendation.
D.-T. Le, H. W. Lauw and Y. Fang. In IJCAI 2019, pp. 2808--2814.
[Paper] [Code] [Slides] [Poster]Adversarial Learning on Heterogeneous Information Networks.
B. Hu, Y. Fang and C. Shi. In KDD 2019, pp. 120--129.
[Paper] [Code] [Poster] [中文概述]Heterogeneous Embedding Propagation for Large-scale E-Commerce User Alignment.
V. W. Zheng, M. Sha, Y. Li, H. Yang, Y. Fang, Z. Zhang, K.-L. Tan and K. C.-C. Chang. In ICDM 2018 (Short), pp. 1434--1439.
[Paper] [Slides] [Poster]Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation.
D.-T. Le, H. W. Lauw and Y. Fang. In IJCAI 2018, pp. 3414--3420.
[Paper] [Code] [Slides] [Poster]Region Average Pooling for Context-Aware Object Detection.
K. Kuan, G. Manek, J. Lin, Y. Fang and V. Chandrasekhar. In ICIP 2017, pp. 1347--1351.
[Paper]Object Detection Meets Knowledge Graphs.
Y. Fang, K. Kuan, J. Lin, C. Tan and V. Chandrasekhar. In IJCAI 2017, pp. 1661--1667.
[Paper] [Code] [Slides] [Poster]Basket-Sensitive Personalized Item Recommendation.
D.-T. Le, H. W. Lauw and Y. Fang. In IJCAI 2017, pp. 2060--2066.
[Paper] [Slides] [Poster]Modeling Sequential Preferences with Dynamic User and Context Factors.
D.-T. Le, Y. Fang and H. W. Lauw. In ECML-PKDD 2016, pp. 145--161.
[Paper] [Supplementary] [Slides] [Poster]Learning to Query: Focused Web Page Harvesting for Entity Aspects.
Y. Fang, V. W. Zheng and K. C.-C. Chang. In ICDE 2016, pp. 1002--1013.
[Paper] [Slides] [Poster]Semantic Proximity Search on Graphs with Metagraph-based Learning.
Y. Fang, W. Lin, V. W. Zheng, M. Wu, K. C.-C. Chang and X. Li. In ICDE 2016, pp. 277--288.
[Paper] [Code] [Slides] [Poster]Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically.
Y. Fang, K. C.-C. Chang and H. W. Lauw. In ICML 2014 (2), pp. 406--414.
[Paper] [Supplementary] [Data] [Slides]Incremental and Accuracy-Aware Personalized PageRank through Scheduled Approximation.
F. Zhu, Y. Fang, K. C.-C. Chang and J. Ying. In VLDB 2013, 6(6), pp. 481--492.
Extended version invited to the collection of Best Papers of VLDB'13.
[Paper] [Data] [Slides]RoundTripRank: Graph-based Proximity with Importance and Specificity.
Y. Fang, K. C.-C. Chang and H. W. Lauw. In ICDE 2013, pp. 613--624.
[Paper] [Data] [Slides] [Poster]Confidence-Aware Graph Regularization with Heterogeneous Pairwise Features.
Y. Fang, P. Hsu and K. C.-C. Chang. In SIGIR 2012, pp. 951--960.
[Paper] [Slides]Searching Patterns for Relation Extraction over the Web: Rediscovering the Pattern-Relation Duality.
Y. Fang and K. C.-C. Chang. In WSDM 2011, pp. 825--834.
[Paper] [Poster]Privacy beyond Single Sensitive Attribute.
Y. Fang, M. Ashrafi and S.-K. Ng. In DEXA 2011, pp. 187--201.
[PDF]Efficient Skyline Maintenance for Streaming Data with Partially-Ordered Domains.
Y. Fang and C. Y. Chan. In DASFAA 2010, pp. 322--336.
[Paper] [Slides]
Refereed Journal
Dual-View Preference Learning for Adaptive Recommendation.
Z. Liu, Y. Fang*, M. Wu*. To appear in IEEE TKDE.
(* Corresponding author.)
[Paper] [Code]Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation.
Z. Liu, Y. Fang*, M. Wu*. In ACM TOIS 41(3), Article No. 55, 2023.
(* Corresponding author.)
[Paper] [Code]Pre-training Graph Neural Networks for Link Prediction in Biomedical Networks.
Y. Long, M. Wu, Y. Liu, Y. Fang, C.-K. Kwoh, J. Chen, J. Luo and X.-L. Li. In Bioinformatics 38(8), 2022, pp. 2254--2262.
[Paper] [Supplementary] [Code]Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle.
F. Zhu, Y. Fang, K. Zhang, K. C.-C. Chang, H. Cao, Z. Jiang, M. Wu. In IEEE TKDE 35(3), 2023, pp. 3195--3210.
[Paper] [Code]Neighbor-Anchoring Adversarial Graph Neural Networks.
Z. Liu, Y. Fang*, Y. Liu, V. W. Zheng. In IEEE TKDE 35(1), 2023, pp. 784--795.
(* Corresponding author.)
[Paper] [Code]Prediction of Synthetic Lethal Interactions in Human Cancers using Multi-view Graph Auto-Encoder.
Z. Hao, D. Wu, Y. Fang*, M. Wu*, R. Cai* and Xiao-Li Li. In IEEE J. of Biomedical & Health Informatics (JBHI) 25(10), 2021, pp. 4041--4051.
(* Co-corresponding authors.)
[Paper] [Supplementary] [Code]Multi-View Collaborative Network Embedding.
S. Ata, Y. Fang*, M. Wu*, J. Shi, C. Kwoh, X. Li. In ACM TKDD 15(3), Article No. 39, 2021.
(* Co-corresponding authors.)
[Paper] [Code]Recent Advances in Network-based Methods for Disease Gene Prediction.
S. Ata, M. Wu, Y. Fang, L. Ou-Yang, C. Kwoh and X. Li. In Briefings in Bioinformatics 22(4), Article No. bbaa303, 2020.
[Paper] [Code]Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling.
Y. Ji, M. Yin, H. Yang, J. Zhou, V. W. Zheng, C. Shi* and Y. Fang*. In ACM TKDD 15(1), Article No.10, 2020.
(* Co-corresponding authors.)
[Paper] [Code]Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks.
Y. Ji, C. Shi, Y. Fang, X. Kong and M. Yin. In IPM 57(6), 2020.
[Paper] [Code]mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding.
W. Zhang, Y. Fang*, Z. Liu, M. Wu and X. Zhang*. In IEEE TKDE 34(3), 2020, pp. 1317--1329.
(* Co-corresponding authors.)
[Paper] [Code]Dual-Dropout Graph Convolutional Network for Predicting Synthetic Lethality in Human Cancers.
R. Cai, X. Chen, Y. Fang*, M. Wu* and Y. Hao. In Bioinformatics 36(16), 2020, pp. 4458--4465.
(* Co-corresponding authors.)
[Paper] [Code] [Supplementary]Metagraph-based Learning on Heterogeneous Graphs.
Y. Fang, W. Lin, V. W. Zheng, M. Wu, J. Shi, K. C.-C. Chang and X. Li. In IEEE TKDE 33(1), 2019, pp. 154--168.
[Paper] [Code]Integrating Node Embeddings and Biological Annotations for Genes to Predict Disease-Gene Associations.
S. Ata, L. Ou-Yang, Y. Fang, C.-K. Kwoh, M. Wu and X. Li. In BMC Systems Biology 12(Supp 9), 2018, pp. 31--44.
Invited for oral presentation at GIW'18.
[Paper]Disease Gene Classification with Metagraph Representations.
S. Ata, Y. Fang, M. Wu, X. Xiao and X. Li. In Methods 131, 2017, pp. 83--92.
[Paper]Scheduled Approximation for Personalized PageRank with Utility-Driven Hub Selection.
F. Zhu, Y. Fang, K. C.-C. Chang and J. Ying. In VLDBJ 24(5), 2015, Special Issue on Best Papers of VLDB'13, pp. 655--679.
[Paper] [Data]Entity Linking on Microblogs with Spatial and Temporal Signals.
Y. Fang and M.-W. Chang. In TACL 2(Oct), 2014, pp. 259--272.
Invited for oral presentation at EMNLP'14.
[Paper] [Slides] [Data]
Refereed Workshop/Demo/Abstract
Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle (Extended Abstract).
F. Zhu, Y. Fang, K. Zhang, K. C.-C. Chang, H. Cao, Z. Jiang, M. Wu. In ICDE 2022, pp. 1569--1570.
[Extended abstract] [Poster]Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract).
Z. Liu, Y. Fang, Y. Liu, V. W. Zheng. To appear in ICDE 2022, pp. 1571--1572.
[Extended abstract] [Poster]Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. [Abstract only].
Y. Lu, Y. Fang, C. Shi. In China National Conference on Social Media Processing (SMP) 2020.
[Abstract]Network Embedding with Attribute Refinement. [Workshop paper]
T. Xiao, Y. Fang, H. Yang and V. W. Zheng. In KDD Workshop on Mining and Learning with Graphs 2017 (non-archival).
[Paper] [Video]Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text. [Workshop paper]
Z. Wang*, K. Kuan*, M. Ravaut*, G. Manek*, S. Song*, Y. Fang, et al. In CVPR Workshop on YouTube-8M Challenge 2017 (non-archival).
(* Co-first authors with equal contribution.)
Ranked 22/655 in the corresponding Kaggle challenge.
[Paper]ARISE-PIE: A People Information Integration Engine over the Web. [Workshop paper]
V. W. Zheng, T. Hoang, P. Chen, Y. Fang, X. Yang and K. C.-C. Chang. In CIKM Workshop on Data-Driven Talent Acquisition 2016 (non-archival).
[Paper] [Demo]IntelligShop: Enabling Intelligent Shopping in Malls through Location-based Augmented Reality. [Demo paper]
A. Adhikari, V. W. Zheng, H. Cao, M. Lin, Y. Fang and K. C.-C. Chang. In ICDM 2015, pp. (W)1604--1607.
Featured in media coverage in the United States.
[Paper] [Demo] [News]Differences in Plasma Lipids during Rested Wakefulness and Sleep Deprivation. [Abstract only]
S. Huang, E. C.-P. Chua, G. Shui, Y. Fang, S.-C. Yeo, M. R. Wenk and J. J. Gooley. In SLEEP 2015.
[Abstract] [Poster]
Editorial
The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021).
C. Shi, Y. Fang , Y. Ye , J. Zhang. In KDD 2021, pp. 4157--4158.
[PDF]
Book Chapter
Disease Gene Classification with Metagraph Representations.
S. Ata, Y. Fang, M. Wu, X. Li and X. Xiao. In Data Mining for Systems Biology: Methods and Protocols, 2nd Edition, 2018, pp. 211--224. Humana Press, New York, NY.
[Link]
Preprint
End-to-End Video Classification with Knowledge Graphs.
Y. Fang, Zhe Wang, Jie Lin, Luis Fernando D’Haro, Kim Jung Jae, Zeng Zeng and Vijay Chandrasekhar. In arXiv:1711.01714 [cs.CV].
[PDF]