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Simple Multigraph Convolution Networks by Xinjie Shen from South China University of Technology

Existing multigraph convolution methods struggle to effectively balance effectiveness and efficiency. This research introduces Simple Multigraph Convolution Networks (SMGCN) which addresses this issue by leveraging credible graph subgraph-level and edge-level voting from a multigraph. The study compares SMGCN with previous methods like PGCN, MGCN, and MIMO-GCN through experiments. For more details and access to the code, visit https://github.com/frinkleko/SMGCN.

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Simple Multigraph Convolution Networks by Xinjie Shen from South China University of Technology

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  1. This PPT Simple Multigraph Convolution Networks Xinjie Shen South China University of Technology

  2. Simple Multigraph Convolution Networks Existing multigraph convolution methods still have difficult in effectively solving the conflict between effectiveness and efficiency

  3. Simple Multigraph Convolution Networks Previous methods PGCN MGCN MIMO-GCN

  4. Simple Multigraph Convolution Networks Build Credible Graph Subgraph-level Edge-level Edge voting from multigraph

  5. SMGCN Experiments 5

  6. Thank You Here are our codes https://github.com/frinkleko/SMGCN 6

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