Shikhar Vashishth, et.al. “Composition-based Multi-Relational Graph Convolutional Networks”, ICLR 2020
目录
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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: $\sum(W_\lambda [h_i; h_r])$