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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.
Nodes can now autonomously decide how to propagate information, leading to improved adaptability in diverse graph structures.
