NeurIPS 2022 Temporal Graph Learning Workshop

Important Information

NeurIPS virtual site (to follow livestream and recordings)
Workshop Date: Saturday, Dec. 3rd 2022; Room  399
Workshop Time Zone: New Orleans (GMT-5)

Contact Email: tglworkshop2022@gmail.com
Twitter: https://twitter.com/tgl_workshop
join us on slack 

Theme

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 more.

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.

Keynote Talks

Panel

Organizers

Program Committee

Call for Papers

Camera Ready Deadline: Nov. 23rd, 2022, Anywhere on Earth

Submission Deadline: Sep. 23rd, 2022, Anywhere on Earth (extended)
Accept/Reject Notification: Oct. 18th, 2022, Anywhere on Earth

We encourage researchers to submit their papers to this workshop on topics broadly related to the workshop themes above. We also welcome papers that present benchmark datasets, evaluation protocols, and challenges on relevant topics, including but not limited to:

• Learning from temporal knowledge graphs, temporal hypergraphs and other types of evolving graphs

• Data mining and visualization for temporal graphs

• Benchmarking and evaluation of temporal graph learning methods

• Novel temporal graph datasets

• Challenges and solutions for large-scale training and deployment of temporal graph learning methods

• Integration of temporal graphs with other fields such as computer vision, natural language processing, reinforcement learning, financial security, etc.

• Question answering and common sense reasoning with evolving graph structure

• Visual question answering with knowledge graphs

• Theoretical analysis of temporal graphs, their properties and evolution

• Temporal graph applications: anomaly detection, disease modeling, fraud detection, social media, traffic forecasting,  recommendation systems, etc.

• Examples of temporal graph representation learning across the natural and social sciences

As temporal graph learning is a rapidly evolving field, if you are unsure about the relevance of a topic, feel free to reach out to tglworkshop2022@gmail.com for clarification. 

Authors can submit their papers through OpenReview. All submissions should use this template, and submissions should be in .pdf format.

The review process is double-blind, thus the papers should be anonymized appropriately. Previously published work is not accepted. This also includes papers accepted at the main NeurIPS conference this year.

Submissions should be no more than 8 pages (with unlimited pages for references and supplementary materials). We suggest authors only include minor details (e.g., hyperparameter settings) in the supplementary material. Shorter 4-page submissions presenting work in progress or discussing open problems and challenges in the domain of temporal graph learning are also welcome.

All accepted papers will be presented as posters during the workshop and the camera-ready version will be hosted on the workshop website (but not considered archival for resubmission purposes). In addition, four selected papers will be presented as spotlight talks during the workshop and one will receive the Best Paper Award. Authors of accepted papers will be asked to prepare a short video describing their work on SlidesLive which will also be hosted on the website.

Contact Email:   tglworkshop2022@gmail.com

Twitter: https://twitter.com/tgl_workshop