Publications | Dongjie Wang

I have published 25+ papers in prestigious journals and conferences, including DM venues (e.g., KDD*3, WWW*1, ICDM*5, TKDE*2, KAIS*2) and AI venues (e.g., AAAI*3). Among them, I got two best paper runner-ups in SIGSPATIAL’20 and ICDM’21 respectively. The representative papers can be categorized as follows:

Data-centric AI
- Automated Feature Selection Learning: [SDM’23], [ICDM’21], [KAIS’23], [TKDE’23], [ICDM’23]
- Automated Feature Generation Learning: [KDD’22], [ICDM’23] [NeurIPS’23]
Automated Urban Planning
- Generative Adversarial Network based Urban Planner: [SIGSPATIAL’20], [TSAS’23]
- Variational Autoencoder based Urban Planner: [ICDM’21], [KAIS’23]
- Transformer based Urban Planner: [AAAI’23]
Anomaly Detection and Root Cause Analysis
- Anomaly Detection: [ICDM’20]
- Interdependent Causal Graph based RCA: [KDD’23]
- Incremental Causal Update based RCA: [KDD’23]
User Profiling and Recommendation
- User Profiling and POI recommendation: [TKDE’23], [AAAI’21], [TBD’23]

2023

NeurIPS’23

Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions

Dongjie Wang, Meng Xiao, Min Wu, Pengfei Wang, Yuanchun Zhou, and Yanjie Fu

Advances in Neural Information Processing Systems, 2023

Bib

@article{wang2023,
  title = {Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions},
  author = {Wang, Dongjie and Xiao, Meng and Wu, Min and Wang, Pengfei and Zhou, Yuanchun and Fu, Yanjie},
  journal = {Advances in Neural Information Processing Systems},
  volume = {},
  pages = {},
  year = {2023},
}

KDD’23
Interdependent Causal Networks for Root Cause Localization

Dongjie Wang, Zhengzhang Chen, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, and Haifeng Chen

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Abs Bib HTML

The goal of root cause analysis is to identify the underlying causes of system problems by discovering and analyzing the causal structure from system monitoring data. It is indispensable for maintaining the stability and robustness of large-scale complex systems. Existing methods mainly focus on the construction of a single effective isolated causal network, whereas many real-world systems are complex and exhibit interdependent structures (i.e., multiple networks of a system are interconnected by cross-network links). In interdependent networks, the malfunctioning effects of problematic system entities can propagate to other networks or different levels of system entities. Consequently, ignoring the interdependency results in suboptimal root cause analysis outcomes.In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization. REASON consists of Topological Causal Discovery (TCD) and Individual Causal Discovery (ICD). The TCD component aims to model the fault propagation in order to trace back to the root causes. To achieve this, we propose novel hierarchical graph neural networks to construct interdependent causal networks by modeling both intra-level and inter-level non-linear causal relations. Based on the learned interdependent causal networks, we then leverage random walk with restarts to model the network propagation of a system fault. The ICD component focuses on capturing abrupt change patterns of a single system entity. This component examines the temporal patterns of each entity’s metric data (i.e., time series), and estimates its likelihood of being a root cause based on the Extreme Value theory. Combining the topological and individual causal scores, the top K system entities are identified as root causes. Extensive experiments on three real-world datasets validate the effectiveness of the proposed framework.
@inproceedings{10.1145/3580305.3599849, author = {Wang, Dongjie and Chen, Zhengzhang and Ni, Jingchao and Tong, Liang and Wang, Zheng and Fu, Yanjie and Chen, Haifeng}, title = {Interdependent Causal Networks for Root Cause Localization}, year = {2023}, isbn = {9798400701030}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3580305.3599849}, booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {5051–5060}, numpages = {10}, keywords = {interdependent networks, network propagation, graph neural networks, causal structure learning, root cause analysis}, location = {Long Beach, CA, USA}, series = {KDD '23}, }
KDD’23
Incremental Causal Graph Learning for Online Root Cause Analysis

Dongjie Wang, Zhengzhang Chen, Yanjie Fu, Yanchi Liu, and Haifeng Chen

In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Abs Bib HTML

The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure recovery and mitigate system damages or financial losses. However, previous research has mostly focused on developing offline RCA algorithms, which often require manually initiating the RCA process, a significant amount of time and data to train a robust model, and then being retrained from scratch for a new system fault.In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model. CORAL consists of Trigger Point Detection, Incremental Disentangled Causal Graph Learning, and Network Propagation-based Root Cause Localization. The Trigger Point Detection component aims to detect system state transitions automatically and in near-real-time. To achieve this, we develop an online trigger point detection approach based on multivariate singular spectrum analysis and cumulative sum statistics. To efficiently update the RCA model, we propose an incremental disentangled causal graph learning approach to decouple the state-invariant and state-dependent information. After that, CORAL applies a random walk with restarts to the updated causal graph to accurately identify root causes. The online RCA process terminates when the causal graph and the generated root cause list converge. Extensive experiments on three real-world datasets demonstrate the effectiveness and superiority of the proposed framework.
@inproceedings{10.1145/3580305.3599392, author = {Wang, Dongjie and Chen, Zhengzhang and Fu, Yanjie and Liu, Yanchi and Chen, Haifeng}, title = {Incremental Causal Graph Learning for Online Root Cause Analysis}, year = {2023}, isbn = {9798400701030}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, doi = {10.1145/3580305.3599392}, booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {2269–2278}, numpages = {10}, keywords = {causal structure learning, incremental learning, trigger point detection, disentangled graph learning, root cause analysis}, location = {Long Beach, CA, USA}, series = {KDD '23}, }

AAAI’23

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, and Yanjie Fu

In Proceedings of the AAAI Conference on Artificial Intelligence, 2023

2023

2022

2021

2020

2018