About Me

Hi there! I am Yixin Liu, an ARC DECRA Fellow and Lecturer (Tenure-Track Assistant Professor equivalent in the US system) in the School of Information and Communication Technology (ICT), Griffith University, Australia. Before taking up this role, I worked as a Research Fellow at Griffith University starting in 2024. I am a core member of the TrustAGI Lab led by Prof. Shirui Pan. I received my Ph.D. from the Faculty of Information Technology, Monash University, in 2024, and obtained my B.S. and M.E. degrees from Beihang University, China, in 2017 and 2020, respectively. I was awarded the Google PhD Fellowship in 2022 as one of five recipients from Australia.

I am looking for self-motivated PhD students and interns! If you are interested in working with me, please fill out this form. You are also welcome to follow up by email after submission. I will review all forms and emails carefully, but I do apologize that I may not be able to reply to every inquiry individually.

My research aims to build intelligent systems that can act (Agentic AI), understand structured worlds (Structural AI), and remain reliable under uncertainty and distribution shifts (Truthworthy AI). Research areas of particular interest include:

🛡️ Truthworthy AI

Anomaly Detection

Anomaly/Fraud Detection on Graph Data: CoLA (TNNLS’21), TADDY (TKDE’21), SL-GAD (TKDE’21), ANEMONE (CIKM’21), PREM (ICDM’23), SIGNET (NeurIPS’23), ARC (NeurIPS’24), FM4GAD Survey (ICKG’25), HUGE (AAAI’25), FreeGAD (CIKM’25), UB-GOLD (ICLR’25), TUNE (AAAI’26)
Anomaly Detection on Text Data: MCA2 (ECML-PKDD’26)
Anomaly Detection on Tabular Data: OFA-TAD (ICML’26)

Learning with Distribution Shift

Graph Out-of-Distribution Detection: GOOD-D (WSDM’23), GOODAT (AAAI’24)
Graph Out-of-Distribution Generalization: MPHIL (TPAMI’26)
Domain Adaptation/Generalization: CLIP4DADG Survey (TPAMI’26)

Federated Learning

General Federated Learning: Survey (ICMLC’24)
Federated Graph Learning: FedSTAR (AAAI’23), FedCIGAR (IJCAI’26),

🕸️ Structural AI

Learning on Graphs

Graph Neural Networks: SUBLIME (WWW’22), CycProp (WWWJ’22), D2PT (KDD’23), DND-NET (KDD’24), SpecG (PAKDD’25), EFGNN (TNNLS’25), Heterophily GNN Survey (TKDE’26)
Graph Self-Supervised Learning: GSSL Survey (TKDE’22), GREET (AAAI’23), MQE (CIKM’24), Position (AI Magazine’24)
Data-Centric Graph Learning: Data-Centric Survey (arXiv’23)
Graphs for LLM: Position (IntellSyst’24)

Learning on Tabular Data

Tabular Data Imputation: SimpDM (CIKM’24)
Tabular Data Anomaly Detection: OFA-TAD (ICML’26)

🤖 Agentic AI

LLM-based Multi-Agent System

Multi-Agent System Design: EIB-Learner (EMNLP’25), ARG-Designer (AAAI’26), OFA-MAS (WWW’26)
Safeguarding Multi-Agent System: BlindGuard (ACL’26), XG-Guard (ACL’26)

LLM-based Agents

Graph-Augmented Agentic System: Survey (IntellSyst’26)
Agentic Memory: Survey (arXiv’25)

News

2026/01: Honored and excited to be appointed as a Lecturer (Assistant Professor equivalent) at Griffith University!
2025/11: Honored and thrilled to receive ARC DECRA Fellowship 2026 (AU$500,386) for supporting my research on graph anomaly detection!
2025/09: Honored to be named in the list of the World’s Top 2% Scientists 2025 by Stanford University/Elsevier.
2026/06: Our paper on graph OOD generalization has been accepted by TPAMI.
2026/05: Our papers on generalist graph anomaly detection and one-for-all tabular data anomaly detection have been accepted by ICML 2026 (2x).
2026/05: Our papers on text-attibuted graph fraud detection and federated graph anomaly detection have been accepted by IJCAI 2026 (2x).
2026/04: Our paper on generalist graph anomaly detection has been accepted by TKDE.
2026/04: Our papers on multi-agent system safeguarding and explainable multi-agent system safeguarding have been accepted by ACL 2026 Main Conference (2x).
2026/05: Our paper on text anomaly detection has been accepted by ECML-PKDD 2026.
2026/03: Our survey on heterophilic graph neural networks has been accepted by TKDE.
2026/02: Our paper on multimodal recommendation has been accepted by PAKDD 2026.
2026/01: Our paper on LLM multi-agent system design has been accepted by WWW 2026.
2025/12: Our survey on domain generalization/adaptation has been accepted by TPAMI.
2025/11: Our papers on LLM multi-agent system design and graph anomaly detection have been accepted by AAAI 2026.
2025/10: Our position paper on graph-augmented LLM agents has been accepted by IEEE Intelligent Systems.
2025/09: Our survey on generalizable graph anomaly detection has been accepted by ICKG 2025.
2025/08: Our paper on LLM multi-agent system design has been accepted by EMNLP 2025 (Main Conference).
2025/08: Our paper on graph anomaly detection has been accepted by CIKM 2025.
2025/06: Our paper on graph neural network has been accepted by TNNLS.
2025/02: Our paper on graph foundation model has been accepted by PAKDD 2025.
2025/01: Our benchmark on graph-level anomaly/OOD detection has been accepted by ICLR 2025.
2024/12: Our paper on graph fraud detection has been accepted by AAAI 2025.
2024/09: Our paper on generalist graph anomaly detection with in-context learning has been accepted by NeurIPS 2024.
2024/08: Our paper on data-efficient graph learning has been accepted by AI Magazine.
2024/07: Our paper on graph representation learning has been accepted by CIKM 2024.
2024/07: Our paper on data imputation has been accepted by CIKM 2024.
2024/05: Our paper on GNN against label noise has been accepted by KDD 2024.
2024/03: Our paper on diffusion model for data imputation has been accepted by ICLR 2024 GenAI4DM Workshop.
2024/02: Our survey on federated learning has been accepted by IJMLC.
2023/12: Our paper on graph OOD detection has been accepted by AAAI 2024.
2023/11: I give an invited talk at LoG Conference 2023 Shanghai meetup.
2023/11: Our paper on graph+LLM has been accepted by IEEE Intelligent Systems.
2023/09: Our paper on explainable graph anomaly detection has been accepted by NeurIPS 2023.
2023/09: Our survey on data-centric graph machine learning is now on arXiv.
2023/09: Our paper on graph anomaly detection has been accepted by ICDM 2023.
2023/06: We present a tutorial on graph self-supervised learning at IJCNN 2023.
2023/05: Our paper on weak information graph learning has been accepted by KDD 2023.
2022/11: Our paper on graph representation learning has been accepted by AAAI 2023.
2022/11: Our paper on federated graph learning has been accepted by AAAI 2023.
2022/10: Our paper on graph OOD detection has been accepted by WSDM 2023.
2022/08: I am honored to receive the Google Ph.D. Fellowship in 2022.
2022/05: Our survey on graph self-supervised learning has been accepted by IEEE TKDE.
2022/01: Our paper on graph structure learning has been accepted by WWW 2022.
2021/11: Our paper on dynamic graph has been accepted by IEEE TKDE.
2021/10: Our paper on graph anomaly detection has been accepted by IEEE TKDE.
2021/08: Our paper on graph anomaly detection has been accepted by CIKM 2021.
2021/06: Our paper on label propagation has been accepted by World Wide Web.
2021/03: Our paper on graph anomaly detection has been accepted by IEEE TNNLS.

Selected Papers

ICML 2026 Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach Paper Code Yujing Liu, Yixin Liu, Yu Zheng, Alan Wee-Chung Liew, Xiaofeng Cao, Shirui Pan

ICML 2026 Towards One-for-All Anomaly Detection for Tabular Data Paper Code Shiyuan Li, Yixin Liu, Yu Zheng, Xiaofeng Cao, Shirui Pan, Heng Tao Shen

TPAMI 2026 MPHIL: Multi-Prototype Hyperspherical Invariant Learning for Graph Out-of-Distribution Generalization Paper Code Xu Shen*, Yixin Liu*, Yili Wang, Rui Miao, Yiwei Dai, Shirui Pan, Xin Wang

WWW 2026 OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models Paper Code Shiyuan Li*, Yixin Liu*, Yu Zheng, Mei Li, Quoc Viet Hung Nguyen, Shirui Pan

ACL 2026 BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks Paper Code Rui Miao*, Yixin Liu*, Yili Wang, Xu Shen, Yue Tan, Yiwei Dai, Shirui Pan, Xin Wang

ACL 2026 Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection Paper Code Junjun Pan, Yixin Liu^✉, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan

IJCAI 2026 FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection Paper Code Yunfeng Zhao*, Yixin Liu*, Qingfeng Chen, Shiyuan Li, Yue Tan, Shirui Pan

IJCAI 2026 CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection Paper Code Junjun Pan, Yixin Liu^✉, Yu Zheng^✉, Lianhua Chi, Alan Wee-Chung Liew, Shirui Pan

AAAI 2026 Assemble Your Crew: Automatic Multi-Agent Communication Topology Design via Autoregressive Graph Generation Paper Code Shiyuan Li, Yixin Liu, Qingsong Wen, Chengqi Zhang, Shirui Pan

AAAI 2026 Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time Paper Code Junjun Pan, Yixin Liu, Chuan Zhou, Fei Xiong, Alan Wee-Chung Liew, Shirui Pan

TKDE 2026 Graph Neural Networks for Graphs with Heterophily: A Survey Paper Code Xin Zheng, Yi Wang, Yixin Liu, Ming Li, Miao Zhang, Di Jin, Philip S. Yu, Shirui Pan

TKDE 2026 From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection Paper Code Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Philip S. Yu, Shirui Pan

ECML-PKDD 2026 Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations Paper Code Yixin Liu, Kehan Yan, Shiyuan Li, Qingfeng Chen, Shirui Pan

ICLR 2025 Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark Paper Code Yili Wang*, Yixin Liu*, Xu Shen*, Chenyu Li*, Kaize Ding, Rui Miao, Ying Wang, Shirui Pan, Xin Wang

IEEE Intelligent Systems 2025 Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects Paper Yixin Liu, Guibin Zhang, Kun Wang, Shiyuan Li, Shirui Pan

EMNLP 2025 Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems Paper Xu Shen*, Yixin Liu*, Yiwei Dai, Yili Wang, Rui Miao, Yue Tan, Shirui Pan, Xin Wang

AAAI 2025 A Label-free Heterophily-guided Approach for Unsupervised Graph Fraud Detection Paper Junjun Pan, Yixin Liu, Xin Zheng, Yizhen Zheng, Alan Wee-Chung Liew, Fuyi Li, Shirui Pan

TNNLS 2025 Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Paper Qingfeng Chen, Shiyuan Li, Yixin Liu, Shirui Pan, Geoffrey Webb, Shichao Zhang

NeurIPS 2024 ARC: A Generalist Graph Anomaly Detector with in-Context Learning Paper Code Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Shirui Pan

CIKM 2024 Self-supervision Improves Diffusion Models for Tabular Data Imputation Paper Code Yixin Liu, Thalaiyasingam Ajanthan, Hisham Husain, Vu Nguyen

CIKM 2024 Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation Paper Code Shiyuan Li*, Yixin Liu*, Qingfeng Chen, Geoffrey I Webb, Shirui Pan

NeurIPS 2023 Towards Self-Interpretable Graph-Level Anomaly Detection Paper Code Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan

KDD 2023 Learning Strong Graph Neural Networks with Weak Information Paper Code Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan

AAAI 2023 Oral Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating Paper Code Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent Lee, Shirui Pan

AAAI 2023 Oral Federated Learning on Non-IID Graphs via Structural Knowledge Sharing Paper Code Yue Tan*, Yixin Liu*, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang

WSDM 2023 Oral GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection Paper Code Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

TKDE 2022 Graph Self-Supervised Learning: A Survey Paper Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu

WWW 2022 Best Paper Award Candidate Towards Unsupervised Deep Graph Structure Learning Paper Code Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

TKDE 2021 Anomaly Detection in Dynamic Graphs via Transformer Paper Code Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent Lee

TNNLS 2021 Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning Paper Code Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis

Education

Ph.D. (2021-2024) at Monash University
M.S. (2017-2020) at Beihang University
B.S. (2013-2017) at Beihang University

Experience

Griffith University, Lecturer and ARC DECRA Fellow, 2026-present.
Griffith University, Postdoctoral Research Fellow, 2024-2026.
Amazon, Applied Scientist Intern, 2023.
Monash University, Research Assistant, 2021.
Alibaba, Research Intern, 2020.

Contact

Email: yixin.liu[at]griffith[dot]edu[dot]au
Office: N44 1.23, 170 Kessels Road, Nathan, QLD 4111

Yixin Liu (刘奕鑫)