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Graph Geometry Interaction Learning
Shichao Zhu · Shirui Pan · Chuan Zhou · Jia Wu · Yanan Cao · Bin Wang

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1121

While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the flexibility to model intrinsic complex graph geometry. To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph. GIL captures a more informative internal structural features with low dimensions while maintaining conformal invariance of each space. Furthermore, our method endows each node the freedom to determine the importance of each geometry space via a flexible dual feature interaction learning and probability assembling mechanism. Promising experimental results are presented for five benchmark datasets on node classification and link prediction tasks.

Author Information

Shichao Zhu (Institute of Information Engineering, Chinese Academy of Sciences)
Shirui Pan (Monash University)
Chuan Zhou (Chinese Academy of Sciences)
Jia Wu (Macquarie University)
Yanan Cao (Institute of Information Engineering, Chinese Academy of Sciences)
Bin Wang (Xiaomi AI Lab)

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