Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models | Paper Citation Record

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Borgwardt, Cheng Soon Ong, Stefan Schönauer, S

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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This paper cites Bridging Input Feature Spaces Towards Graph Foundation Models.

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Bridging Input Feature Spaces Towards Graph Foundation Models

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Kipf and Max Welling

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models One for All: Towards Training One Graph Model for All Classification Tasks

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Graph Foundation Models: Concepts, Opportunities and Challenges

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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This paper cites McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel.

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel

Reference 23

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models TUDataset: A collection of benchmark datasets for learning with graphs

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Hamilton, Pietro Liò, Yoshua Bengio, and R

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment

Reference 34

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Graph Foundation Models: A Comprehensive Survey

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Chawla, Chuxu Zhang, and Yan- fang Ye

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Cohen, and Ruslan Salakhutdinov

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Relation-Aware Graph Foundation Model

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Fully-inductive Node Classification on Arbitrary Graphs

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models InProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9-13, 2015

Reference 2015

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Unresolved cited work

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