Research

My research spans a wide range of topics around graph neural networks. I am particularly interested in temporal (dynamic) graph neural networks (TGNs), neural differential equations (NDEs), spectral graph theory, and geometric deep learning.


Selected Publications

2022

  1. NIPS
    Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
    Jin Ming, Li Yuan-Fang, Pan Shirui
    The Conference on Neural Information Processing Systems
    PDF Code Poster Slides
  2. TKDE
    Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
    Jin Ming, Zheng Yu, Li Yuan-Fang, Chen Siheng, Yang Bin, Pan Shirui
    IEEE Transactions on Knowledge and Data Engineering
    arXiv Code
  3. TKDE
    Graph Self-Supervised Learning: A Survey
    Liu Yixin*,  Jin Ming*, Pan Shirui, Zhou Chuan, Zheng Yu, Xia Feng, Yu Philip
    IEEE Transactions on Knowledge and Data Engineering
    arXiv
  4. TNNLS
    Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
    Zheng Yizhen,  Jin Ming, Pan Shirui, Li Yuan-Fang, Peng Hao, Li Ming, Li Zhao
    IEEE Transactions on Neural Networks and Learning Systems
    arXiv

2021

  1. IJCAI
    Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
    Jin Ming, Zheng Yizhen, Li Yuan-Fang, Gong Chen, Zhou Chuan, Shirui Pan
    International Joint Conference on Artificial Intelligence
    arXiv Code Poster Slides
  2. CIKM
    ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
    Jin Ming, Liu Yixin, Zheng Yu, Chi Lianhua, Li Yuan-Fang, Pan Shirui
    International Conference on Information & Knowledge Management
    PDF Code Poster Slides
  3. TKDE
    Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
    Zheng Yu,  Jin Ming*, Liu Yixin, Chi Lianhua, Phan Khoa T, Chen Yi-Ping Phoebe
    IEEE Transactions on Knowledge and Data Engineering
    arXiv Code