Hanchen Wang

About Me

I am currently an ARC DECRA Fellow and lecturer at Australian Artificial Intelligence Institute (AAII), University of Technology Sydney, where I serve as Co-Director of the Sydney Data Intelligence Lab (SyDI Lab) together with Prof. Lu Qin. I am also an Adjunct Lecturer at The University of New South Wales and an Associate Investigator of ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS). Before joining UTS, I was a postdoctoral research fellow at Data and Knowledge Research Group at The University of New South Wales, supervised by Prof. Wenjie Zhang. Find out more about me here.

Email: hanchen.wang [at] uts.edu.au

Multivariate Time Series Forecasting needs Cross Variable Loss

Sep 25, 2026

HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis

Sep 25, 2026

🎉 Two papers accepted by NeurIPS 2026

We have two papers accepted by NeurIPS 2026: "Multivariate Time Series Forecasting needs Cross Variable Loss" in the Main Track (congrats to Kuiye) and "HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis" in the Evaluations and Datasets Track (congrats to Han).

Sep 25, 2026

SARL: A Scalable Attribute-Informed Representation Learning Framework for Bipartite Graphs

Sep 13, 2026

🎉 One paper accepted by TKDE 2026

We have one paper, titled "SARL: A Scalable Attribute-Informed Representation Learning Framework for Bipartite Graphs", accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE) 2026. Congrats to Jianke.

Sep 13, 2026

UniCom: A Unified Framework for Community Search and Detection via Transfer Learning

Aug 6, 2026

FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation

Aug 6, 2026

🎉 Two papers accepted by IEEE ICDM 2026

We have two papers accepted by IEEE ICDM 2026 (CORE A*): "UniCom: A Unified Framework for Community Search and Detection via Transfer Learning" (congrats to Yifan) and "FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation" (congrats to Runze).

Aug 6, 2026

🎉 Elected to the UTS FEIT Faculty Board

I have been elected as an academic staff member (representing the School of Computer Science) of the Faculty Board in Engineering and Information Technology (FEIT) at UTS. The term commences on 1 January 2027.

Aug 6, 2026

Mining Discriminative Salient Objects with Optimal Transport for Few-Shot Image Classification

Jul 15, 2026

Algebraic Subgraph Counting

Jun 13, 2026

🎉 One paper accepted by SIGMOD 2027

We have one paper, titled "Algebraic Subgraph Counting", accepted by ACM SIGMOD 2027 (Research Round 1). Congrats to Qiuyu.

Jun 13, 2026

WOCD: A semi-supervised method for overlapping community detection using weak cliques

Jun 11, 2026

OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios

Jun 1, 2026

HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning

May 20, 2026

🎉 One paper accepted by ACM CCS 2026

We have one paper, titled "HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning", accepted by ACM CCS 2026. Congrats to Han.

May 20, 2026

🎉 Agentic AI Survey & ICDE 2026 Tutorial

We released a comprehensive survey "Data in Agentic AI" and a corresponding ICDE 2026 tutorial "Data-Centric Foundations of Agentic AI". Congrats to Yuxin.

May 15, 2026

Towards Generative Graph Matching for Graph Edit Distance Computation

May 1, 2026

Multi-Agent Coordination Adaptation via Structure-Guided Orchestration

May 1, 2026

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model

May 1, 2026

EXG: Self-Evolving Agents with Experience Graphs

May 1, 2026

Data-Centric Foundations of Agentic AI

May 1, 2026

🎉 One paper accepted by ICML 2026

We have one paper, titled "Towards Generative Graph Matching for Graph Edit Distance Computation", accepted by ICML 2026. Congrats to Wei.

May 1, 2026

MGDN: A Graph of Graphs Neural Network for Malware Detection

Apr 30, 2026

Data in Agentic AI: A Comprehensive Survey

Apr 1, 2026

🎉 One paper accepted by TKDE 2026

We have one paper, titled "MGDN: A Graph of Graphs Neural Network for Malware Detection", accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE) 2026.

Apr 1, 2026

Accelerating K-Core Computation in Temporal Graphs

Mar 1, 2026

Small Shifts, Large Gains: Unlocking Traditional TSP Heuristic Guided-Sampling via Unsupervised Neural Instance Modification

Feb 1, 2026

RLMiner: Finding the Most Frequent k-sized Subgraph via Reinforcement Learning

Jan 1, 2026

Finding critical users in social networks with reinforcement learning

Jan 1, 2026

Algorithmic and Learning-based Approaches to Subgraph Matching and Counting: A Survey

Jan 1, 2026

Computing Historical k-Core in Parallel

Dec 4, 2025

🎉 One paper accepted by EDBT 2026

We have one paper, titled "Accelerating K-Core Computation in Temporal Graphs", accepted by EDBT 2026. Congrats to Zhuo.

Dec 1, 2025

🎉 One paper accepted by Information Sciences

We have one paper, titled "Finding critical users in social networks with reinforcement learning", accepted by Information Sciences. Congrats to Xulu.

Nov 1, 2025

Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications

Oct 1, 2025

Give Me Some SALT: Structure-Aware Link Modeling for Temporal Weighted Link Prediction

Oct 1, 2025

🎉 One paper accepted by NeurIPS 2025

We have one paper, titled "Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN", accepted by NeurIPS 2025. Congrats to Wei.

Sep 20, 2025

Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN

Sep 19, 2025

Machine Learning for Graph Data Management and Query Processing

*International Conference on Very Large Data Bases (VLDB) 2025 (Tutorial)*

Sep 4, 2025

🎉 One paper accepted by CIKM 2025

We have one paper, titled "Give Me Some SALT Structure-Aware Link Modeling for Temporal Weighted Link Prediction", accepted by CIKM 2025. Congrats to Ting.

Aug 6, 2025

Efficient and Accurate Subgraph Counting: A Bottom-up Flow-learning based Approach

Aug 1, 2025

🎉 VLDB Tutorial

We have one tutorial, titled "Machine Learning for Graph Data Management and Query Processing", accepted by VLDB 2025. I will be the presenter. See you in London.

Jun 30, 2025

Structure and Position-Aware Graph Modeling for Trajectory Similarity Computation Over Road Networks

May 1, 2025

IGFM: An Enhanced Graph Similarity Computation Method with Fine-Grained Analysis

Apr 8, 2025

Predicting Membrane Fouling of Submerged Membrane Bioreactor Wastewater Treatment Plants Using Machine Learning

Mar 5, 2025

AIGC for Graphs: Current Techniques and Future Trends

*IEEE International Conference on Data Engineering (**ICDE**) 2025 (Tutorial)*

Mar 4, 2025

DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching

Mar 1, 2025

Covering K-Cliques in Billion-Scale Graphs

Mar 1, 2025

🎉 One paper accepted by VLDB 2025

We have one paper, titled "Efficient and Accurate Subgraph Counting: A Bottom-up Flow-learning based Approach", accepted by VLDB 2025. Congrats to Qiuyu.

Mar 1, 2025

AI-Empowered Catalyst Discovery: A Survey from Classical Machine Learning Approaches to Large Language Models

Feb 1, 2025