Selected Publications
Tutorials
Yulong Pei, Pengfei Jiao, Xuan Guo, George Fletcher, Mykola Pechenizkiy. Roles Analytics in Networks – Foundations, Methods and Applications. In ICDM, 2021.
Yulong Pei, Pengfei Jiao, Akrati Saxena, Xuan Guo, George Fletcher, Mykola Pechenizkiy. Roles in Networks - Foundations, Methods and Applications. In DSAA, 2021.
Yulong Pei, George Fletcher, Mykola Pechenizkiy. Role Analytics in Networks. In ECML/PKDD, 2020.
Journals
Pengfei Jiao, Xuan Guo, Ting Pan, Wang Zhang, Yulong Pei. A Survey on Role-Oriented Network Embedding. In IEEE Transactions on Big Data, Accepted.
Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy. ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks. In Machine Learning, 2021.
Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy. struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding. In Data Mining and Knowledge Discovery, 2020.
Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy. Exceptional Spatio-Temporal Behavior Mining through Bayesian Non-Parametric Modeling. In Data Mining and Knowledge Discovery, 2020.
Jianpeng Zhang, Yulong Pei, George Fletcher, Mykola Pechenizkiy. Evaluation of the Sample Clustering Process on Graphs. In IEEE Transactions on Knowledge and Data Engineering, 2019.
Conferences
Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy. On Generalization of Graph Autoencoders with Adversarial Training. In ECML/PKDD, 2021.
Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy. Calibrated Adversarial Training, In ACML, 2021.
Shiwei Liu, Decebal C. Mocanu, Yulong Pei, Mykola Pechenizkiy. Selfish sparse RNN training. In ICML, 2021.
Yulong Pei, Qian Zhang. GOAT at the FinSim-2 task: Learning Word Representations of Financial Data with Customized Corpus. In WWW Companion, 2021.
Yulong Pei, Fang Lyu, Werner van Ipenburg, Mykola Pechenizkiy. Subgraph Anomaly Detection in Financial Transaction Networks. In ACM International Conference on AI in Finance (ICAIF), 2020.
Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy. Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data. In AAAI, 2020.
Yulong Pei, George Fletcher, Mykola Pechenizkiy. Joint Role and Community Detection in Networks via L2,1 Norm Regularized Nonnegative Matrix Tri-Factorization. In ASONAM, 2019.
Yulong Pei, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy. DyNMF: Role Analytics in Dynamic Social Networks. In IJCAI, 2018.
Wouter Lightenberg, Yulong Pei, George Fletcher, Mykola Pechenizkiy. Tink: a temporal graph analytics library for Apache Flink. Poster in WWW (Companion Volume), 2018.
Yulong Pei, Nilanjan Chakraborty, Katia Sycara. Nonnegative Matrix Tri-factorization with Graph Regularization for Community Detection in Social Networks. In IJCAI, 2015.
Wenpeng Yin, Yulong Pei. Optimizing Sentence Modeling and Selection for Document Summarization. In IJCAI, 2015.
Pengtao Xie, Yulong Pei, Yuan Xie, Eric Xing. Mining User Interests from Personal Photos. In AAAI, 2015.
Yulong Pei, Wenpeng Yin, Qifeng Fan, Lian’en Huang. A supervised aggregation framework for multi-document summarization. In COLING, 2012.
Workshops
Zeyu Zhang, Yulong Pei. A Comparative Study on Robust Graph Neural Networks to Structural Noises. In Deep Learning on Graphs: Methods and Applications (DLG-AAAI’22), 2022.
Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy. Semantic-Based Few-Shot Learning by Interactive Psychometric Testing. In AAAI 2022 Workshop on Interactive Machine Learning, 2022.
Yulong Pei, Xin Du, George Fletcher, Mykola Pechenizkiy. Dynamic Network Representation Learning via Gaussian Embedding. In NeurIPS 2019 Workshop on Graph Representation Learning, 2019.
Yulong Pei, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy. Node Classification in Dynamic Social Networks. In ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (AALTD), 2016.
Preprint
- Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy. Hop-count based self-supervised anomaly detection on attributed networks.