Soft-Isomorphic Relational Graph Convolution Network (SIR-GCN)

This is the official repository for the paper Contextualized Messages Boost Graph Representations.

Method

SIR-GCN emphasizes the non-linear and contextualized transformation of neighborhood features to control and guide hash (aggregation) collisions when the space of node feature is uncountable. The model may be expressed as

$$\boldsymbol{h_u^*} = \sum_{v \in \mathcal{N}(u)} \boldsymbol{W_R} ~ \sigma\left(\boldsymbol{W_Q} \boldsymbol{h_u} + \boldsymbol{W_K} \boldsymbol{h_v}\right),$$

where $\sigma$ is a non-linear activation function. Leveraging linearity, the model has a computational complexity of

$$\mathcal{O}\left(\left|\mathcal{N}\right| \times d_{\text{hidden}} \times d_{\text{in}} + \left|\mathcal{E}\right| \times d_{\text{hidden}} + \left|\mathcal{N}\right| \times d_{\text{out}} \times d_{\text{hidden}}\right).$$

Experiments

All experiments are conducted on a single Nvidia Quadro RTX 6000 (24GB) card using the Deep Graph Library (DGL) with PyTorch backend.