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New Frontiers in Graph Learning (GLFrontiers)

Jiaxuan You · Rex Ying · Hanjun Dai · Ge Liu · Azalia Mirhoseini · Smita Krishnaswamy · Chaoran Cheng

Hall C2 (level 1 gate 9 south of food court)

Fri 15 Dec, 7 a.m. PST

Overview: Graph learning has grown into an established sub-field of machine learning in recent years. Researchers have been focusing on developing novel model architectures, theoretical understandings, scalable algorithms and systems, and successful applications across industry and science regarding graph learning. With the success of the New Frontiers in Graph Learning (GLFrontiers) Workshop in NeurIPS 2022, we hope to continue to promote the exchange of discussions and ideas regarding the future of graph learning in NeurIPS 2023.Challenges: Despite the success of graph learning in various applications, the recent machine learning research trends, especially the research towards foundation models and large language models, have posed challenges for the graph learning field. For example, regarding the model architecture, Transformer-based models have been shown to be superior to graph neural networks in certain small graph learning benchmarks. In terms of usability, with language as a generic user interface, it is still a research frontier to explore whether natural language can also interact with ubiquitous graph-structured data and whether it is feasible to build generic foundation models for graphs. Lastly, while graph learning has achieved recent exciting results in molecule and protein design, exploring how graph learning can accelerate scientific discoveries in other disciplines remains an open question.Goal: The primary goal of this workshop is to expand the impact of graph learning beyond the current boundaries. We believe that graph, or relation data, is a universal language that can be used to describe the complex world. Ultimately, we hope graph learning will become a generic tool for learning and understanding any type of (structured) data. In GLFrontiers 2023, we specifically aim to discuss the future of graph learning in the era of foundation models and envision how graph learning can contribute to scientific discoveries.

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Timezone: America/Los_Angeles