Composition-based Multi-Relational Graph Convolutional Networks

Shikhar Vashishth, et.al. “Composition-based Multi-Relational Graph Convolutional Networks”, ICLR 2020

1 简介

  • 姓名:CompGCN
  • 流派:relational graph embedding
  • 方法:introduce relation embedding into GCN layer
    • build graph: introduce “inverse relation” and self-loop
    • node update: $h_v^{k+1} = f\bigg(\sum_{(u,r)\in\mathcal{N}(v)} W^k_{\lambda(r)} \phi (h_u^k, h_r^k)\bigg)$
    • rel update: $h_r^{k+1} = W_{\text{rel}}^k h_r^k$
    • where $W_{\lambda(r)}$ is a relation-type specific parameter, different for rel / inverse-rel / self-loop
    • trick: the dimention of initial relation representation is reduced into {relation_num}, by expressing it as a linear combination of a set of basis vectors

2 思考

Some relational GCNs(as far as I know):

  • RGCN: $\sum(W_r h_i)$
  • add relational weights into adjacent matrix
  • CompGCN: $\sum(W_\lambda [h_i; h_r])$

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