Yuan FANG @ SMU

Yuan FANG, Ph.D.

Associate Professor of Computer Science
Lee Kong Chian Fellow
School of Computing and Information Systems
Singapore Management University

Office: SCIS2 Room 4053 | Email: yfang "at" smu.edu.sg

Short Biography

I am currently an Associate Professor (tenured) at the School of Computing and Information Systems, Singapore Management University (SMU). Prior to joining SMU, I was a data scientist at DBS Bank, and a research scientist at A*STAR. I obtained a PhD Degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014 on a fully funded scholarship from A*STAR, and Bachelor of Computing with First Class Honors from National University of Singapore in 2009 as the top student in Computer Science. My full CV can be accessed here.

Research Interests

My general interest lies in the broad areas of data mining, machine learning and artificial intelligence. More specifically, I am working on the following research topics: (1) Learning on graphs and information networks; (2) Web and social media analytics; (3) Recommendation systems; (4) Application of knowledge graphs on data mining problems. More details can be found in my research statement, the projects page, and the publications page.

Recent Updates

[05/2025] ✅ We have four papers accepted by KDD 2025 (February cycle).

[04/2025] 🎤 I am giving an invited talk "Would prompt work for graph learning? An exploration of few-shot learning on graphs" at the International Workshop on Resource-Efficient Learning for the Web, colocated with the WWW'25, Sydney, Australia. [PDF]

[04/2025] 🎤 We organized a tutorial titled "Few-Shot Learning on Graphs: From Meta-Learning to LLM-empowered Pre-Training and Beyond" at WWW’25 held in Sydney, Australia [Slides] [Resources].

[04/2025] 🎤 I am giving an invited online talk "Would prompt work for graph learning? An exploration of few-shot learning on graphs" at UQ-DS Seminar, University of Queensland.

[01/2025] ✅ Our position paper on graph foundation models have been accepted by IEEE TPAMI [PDF].

[01/2025] ✅ We have a paper accepted by ICLR 2025, on conditional prompt learning for dynamic graphs [PDF].

[01/2025] ✅ We have a paper accepted by NAACL 2025, on LLM-based heterophilic graph learning [PDF].

[01/2025] ✅ We have a paper accepted by WWW 2025, on text-free graph foundation models [PDF].

[12/2024] ✅ We have a paper accepted by IEEE/ACM TCBB, on a graph neural networks-based approach for predicting anti-cancer drug synergy [PDF].

[12/2024] ✅ We have a paper accepted by Neural Networks, on explainable graph neural networks [PDF].

[12/2024] ✅ We have a paper accepted by AAAI 2025 on graph few-shot learning [PDF].

[11/2024] ✅ We have a paper accepted by KDD 2025 (August cycle) on non-homophilic graph pre-training and prompt learning [PDF].

[11/2024] 🎓 Congratulations to Dr. Zhongzhou Liu on his successful defense of his PhD dissertation titled "Towards trustworthy recommendation systems: Beyond user-item collaborative filtering."

[10/2024] ✅ We have a paper accepted by WACV 2025, on open-world zero-shot learning [PDF].

[10/2024] ✅ We have a paper accepted by Neural Networks, on end-to-end bi-objective graph partitioning [PDF].

[10/2024] 🏆 Our paper on GraphPrompt [PDF] is ranked as the Top 1 Most Influential Papers in WWW'23 by Paper Digest (2024-09 Version).

[10/2024] ✅ We have two papers accepted by WSDM 2025, on contrastive review-centric recommendation [PDF], and aspect-aware review-based recommendation [PDF].

[09/2024] 🏆 I am honored to be recognized among the World's Top 2% Scientist (Single Year, 2024) by Stanford/Elsevier!

[09/2024] ✅ We have three papers accepted by EMNLP 2024, on adapter tuning for few-shot relation learning on knowledge graphs [PDF], out-of-scope intent classification (Findings) [PDF], and a survey on ontology expansion for conversational understanding [PDF].

[08/2024] 🎤 I am giving an invited online talk "Would prompt work for graph learning? An exploration of few-shot learning on graphs" at CRUISE Seminar, University of New South Wales, Australia.

[07/2024] ✅ We have two papers accepted by IEEE TKDE, on generalized graph prompt [PDF] and prompt tuning for text-attributed graphs [PDF].

[07/2024] ✅ We have a paper accepted by CIKM 2024, on large language models for recommendation [PDF].

[05/2024] ✅ We have a paper accepted by KDD 2024, on geodesic distance-based augmentation on graphs [PDF].

[05/2024] ✅ We have a paper accepted by ACL Findings 2024, on parameter-efficient fine-tuning [PDF].

[05/2024] 🎤 We are organizing a tutorial titled "Towards Graph Foundation Models" at WWW 2024 held in Singapore [Part I, Part II & Part III] [Resources].

[05/2024] 🏆 Our paper on GraphPrompt [PDF] ranks among the Top 5 Most Influential Papers in WWW'23 by Paper Digest (2024-05 Version).

[04/2024] ✅ We have two papers accepted by IJCAI 2024, on general graph matching [PDF], and heterogeneous graph transformer [PDF].

[01/2024] ✅ We have three papers accepted by WWW 2024, on multi-task graph prompt [PDF], simple transformer for dynamic graphs [PDF], and diffusion-based negative sampling on graphs [PDF].

[01/2024] 🎤 I am giving an invited online talk "Would prompt work for graph learning? An exploration of few-shot learning on graphs" at VALSE Webinar Series [Slides].