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School of Computer Science and Technology Tianjin University
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SliGFM achieves a breakthrough in graph learning by ensuring that heterogeneous features are not only unified but also semantically rich and transferable across domains.
Overcome the heterogeneity hurdle: MUG pre-trains a single graph encoder that transfers across diverse heterogeneous graphs by unifying node/relation types and learning consistent structural patterns across meta-paths.
Achieve SOTA in cross-domain graph few-shot learning by adaptively aligning diverse graph features into a shared semantic space.
