Ruihong Qiu

邱瑞鸿 /`ray hong chill/

I am currently a Lecturer/Assistant Professor and an ARC DECRA fellow (2025-2027) in School of Electrical Engineering and Computer Science (EECS) at The University of Queensland (UQ). Here is my short bio.

My research focuses on data science methods, including theory and application, such as graph neural networks, information retrieval, recommender systems, time series etc.:

Graph Neural Networks. Text-attributed graph and multimodal graph with (M)LLMs; Graph foundation models; Robustness and scalability of graph learning (OOD, test-time, condensation, distillation, graph-MLP etc.).
Recommendations and Information Retrieval. RL for recommendations; Rich side-information in recommendations with (M)LLMs; Domain-specific IR and document understanding with LLMs.
Time Series. Foundation models for time series; LLMs for time series; Time series for cross-disciplinary applications.

Recruitment

I am actively looking for (1-2) self-motivated PhD students in Year 2026. All fully funded! [For prospective PhD students], [博士招生中文], [For master theses/bachelor honours students at UQ] and [For interns/visitors].

Selected Research

Google Scholar page includes the full publication list.

Out-of-distribution on Graphs

GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
Danny Wang, Ruihong Qiu, Guangdong Bai, Zi Huang
ICLR 2025 (Spotlight)
arXiv / code

We propose the GOLD framework for graph OOD detection, an implicit adversarial learning pipeline with synthetic OOD exposure without pre-trained models.

Graph Condensation

GCondenser: Benchmarking Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang
Preprint
arXiv / code

We introduce a benchmark for graph condensation with a thorough methodology development method and an extensive evaluation protocol.

PUMA: Efficient Continual Graph Learning with Graph Condensation
Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin, Zi Huang
TKDE 2024
arXiv / code

We extend the Condense-and-Train (CaT) continual graph learning algorithm with a more efficient and effective, psudo-label guided memory bank (PUMA🐆) framework.

CaT: Balanced Continual Graph Learning with Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang
ICDM 2023
arXiv / code

We introduce a Condense-and-Train (CaT🐱) memory-based continual graph learning algorithm using graph condensation to construct a more representative memory bank. And a Train-in-Memory continual learning scheme can further alleviate the imbalanced training issue in Class Incremental Learning.

Legal Case Retrieval

CaseLink: Inductive Graph Learning for Legal Case Retrieval
Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang
SIGIR 2024
arXiv / code

We introduce an inductive graph learning paradigm for legal case retrieval to tackle the challenge of unseen testing query and candidate cases.

CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph Augmentation
Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li, Zi Huang
TOIS 2024 (under review)
arXiv / code

We introduce an extended CaseGNN++ method with graph augmentations based on the CaseGNN framework.

CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs
Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li, Zi Huang
ECIR 2024
arXiv / code

We introduce a structural modelling of law case for effective retrieval with the aid of summarisation from LLM and grpah neural networks.

Recommender Systems

	Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation Ruihong Qiu, Zi Huang, Hongzhi Yin ICDM 2022 arXiv / code / video We introduce shared input-output embedding Recurrent Neural Tangent Kernel to sequential recommendation (OverRec).
	Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation Ruihong Qiu, Zi Huang, Hongzhi Yin, Zijian Wang WSDM 2022 arXiv / code We discover and find the cause of representation degeneration problem in sequential recommendation (DuoRec). A contrastive learning regularisation is applied to enforce the distribution to be uniform.
	Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation Ruihong Qiu, Zi Huang, Hongzhi Yin ICDM 2021 arXiv / code / video We introduce multi-instance NCE loss to enhance the side-information based item representation learning (MMInfoRec) in sequential recommendation.
	CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang ACM MM 2021 (oral) arXiv / code / video We introduce a structural causal graph to debias the visual bias in item recommendation (CausalRec).
	Exploiting Positional Information for Session-based Recommendation Ruihong Qiu, Zi Huang, Tong Chen, Hongzhi Yin TOIS 2021 arXiv We introduce a dual positional encoding to theoretically characterise and represent the positional information (PosRec) in session-based recommendation.
	GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen SIGIR 2020 arXiv / code / video We introduce a global attributed graph (GAG) neural network for streaming session-based recommendation.
	Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin TOIS 2020 arXiv We introduce a global graph to model cross session information in session-based recommendation.
	Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin CIKM 2019 arXiv / code We introduce a Full Graph Neural Network (FGNN) for to model a session as graph in session-based recommendation.

Water Management with Data Science

An integrated first principal and deep learning approach for modeling nitrous oxide emissions from wastewater treatment plants
Kaili Li, Haoran Duan, Linfeng Liu, Ruihong Qiu, Ben van den Akker, Bing-Jie Ni, Tong Chen, Hongzhi Yin, Zhiguo Yuan, Liu Ye
Environmental Science & Technology 2022
PDF

We use sequential modelling in deep learning to predict the amount of emitted N₂O with green-house effect.

Team

Boyu Luo, UQ EECS PhD (7.2024-, co-advise with Helen Huang and Guangdong Bai)
Yan Jiang, UQ EECS PhD (1.2024-, co-advise with Helen Huang and Guangdong Bai)
Danny Wang, UQ EECS PhD (1.2024-, co-advise with Helen Huang and Guangdong Bai)
Hrishikesh Patel, UQ EECS PhD (4.2023, co-advise with Sen Wang)
Yi Zhang, UQ EECS PhD (4.2023-, co-advise with Sen Wang and Jiajun Liu)
Yilun Liu, UQ EECS PhD (1.2023-, co-advise with Helen Huang)
Jingyu Ge, UQ ACWEB PhD (1.2022-, co-advise with Zhiguo Yuan, Helen Huang, and Jiuling Li)

Service

Conference organisation: Area Chair at NLPCC’25; PhD Symposium Co-Chair at WWW’25; Program Committee Co-Chair at ADC’23; PhD Forum Co-Chair at AJCAI’23

MISC

I speak Cantonese, Mandarin and English.

Updated on 12/2/2025.