Related papers: Synergistic Graph Fusion via Encoder Embedding

Synergistic Graph Fusion via Encoder Embedding

URL: http://arxiv.org/abs/2303.18051v4
Date: Wed, 5 Jun 2024 09:26:44 GMT
Title: Synergistic Graph Fusion via Encoder Embedding
Authors: Cencheng Shen, Carey E. Priebe, Jonathan Larson, Ha Trinh,
Abstract summary: We introduce a method called graph fusion embedding, designed for multi-graph embedding with shared sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect. Our comprehensive simulations and real data experiments provide compelling evidence supporting the effectiveness of our proposed method.
Score: 16.20934021488478
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect: for sufficiently large vertex size, the accuracy of vertex classification consistently benefits from the incorporation of additional graphs. We establish the mathematical foundation for the method, including the asymptotic convergence of the embedding, a sufficient condition for asymptotic optimal classification, and the proof of the synergistic effect for vertex classification. Our comprehensive simulations and real data experiments provide compelling evidence supporting the effectiveness of our proposed method, showcasing the pronounced synergistic effect for multiple graphs from disparate sources.

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