# Temporal Graph Learning Workshop

Graphs are prevalent in many diverse applications including Social networks, Natural Language Processing, Computer Vision, the World Wide Web, Political Networks, Computational finance, Recommender Systems and more. Graph machine learning algorithms have been successfully applied to various tasks, including node classification, link prediction and graph clustering. However, most methods assume that the underlying network is static thus limiting their applications to real-world networks which naturally evolve over time. On the one hand, temporal characteristics introduce substantial challenges compared to learning on static graphs.  For example, in temporal graphs, the time dimension needs to be modelled jointly with graph features and structures. On the other hand, recent studies demonstrate that incorporating temporal information can improve the prediction power of graph learning methods thus creating new opportunities in applications such as recommendation system, event forecasting, fraud detection and more.

Investigation of temporal graphs provides the backbone of analysis of many different tasks including anomaly or fraud detection, disease modeling, recommendation systems, traffic forecasting, biology, social media, and many more. Hence, there has been a surge of interest in the development of temporal graph learning methods, from diverse domains spanning Machine Learning, Artificial Intelligence, Data Mining, Network Science, Public Health and beyond.

This workshop bridges the conversation among different areas such as temporal knowledge graph learning, graph anomaly detection, and graph representation learning. It aims to share understanding and techniques to facilitate the development of novel temporal graph learning methods. It also brings together researchers from both academia and industry and connects researchers from various fields aiming to span theories, methodologies, and applications.


# Topics
We welcome submissions on a wide range of topics, including (but not restricted to):

- Temporal Graph Modelling & Representation Learning: 
    - Temporal Graph, Spatio-Temporal Graph, and Temporal Knowledge Graph Forecasting and Prediction 
    - Temporal Graph Clustering, Community Detection, and Data Mining 
    - Data Augmentation for Temporal Graphs 
    - Hyperbolic Temporal Graphs 
    - Scalability for Temporal Graphs 
    - Multimodal Temporal Graph Learning 
    - Temporal Graph Learning from Streaming and Online Data 
    - Graphs for Multivariate Time Series Forecasting 
    - Generative Modeling for Evolving Data, Synthetic Graph Models and Simulations 
    - Dynamic System Representation and Excited State Dynamics 
- Temporal Graph Theory: 
    - Expressive Power, Generalization 
    - Signal Processing, Spectral Theories, and Spectral Learning 
    - Neuro-Symbolic Temporal Learning 
    - Causal Reasoning over Temporal Graphs 
- Temporal Graph Applications: 
    - Integration of temporal graphs with other fields such as computer vision, natural language processing, reinforcement learning, financial security, etc. 
    - Temporal Graph Modeling of Brain Networks, Molecular Dynamics, Human Action and Motion, E-commerce and Dynamic Finance, etc. 
    - Anomaly Detection, Misinformation Detection, Polarization Detection and Cyber Security for Dynamic Networks 
    - Video Analysis with Temporal Graphs 
    - Recommender and Question Answering Systems based on Temporal Graphs 
    - Fairness, Explainability, Robustness, Privacy 
- Temporal Graph Benchmarking: 
    - Evaluation of Existing Methods and New Evaluation Approaches 
    - Temporal Graph Datasets 
    - Visualization