  Link prediction in structured-data is an important problem for many
applications, especially for recommendation systems. Existing methods focus on
how to learn the node representation based on graph-based structure.
High-dimensional sparse edge features are not fully exploited. Because
balancing precision and computation efficiency is significant for
recommendation systems in real world, multiple-level feature representation in
large-scale sparse graph still lacks effective and efficient solution. In this
paper, we propose a practical solution about graph neural networks called
Attributed Graph Convolutional Networks(AGCN) to incorporate edge attributes
when apply graph neural networks in large-scale sparse networks. We formulate
the link prediction problem as a subgraph classification problem. We firstly
propose an efficient two-level projection to decompose topological structures
to node-edge pairs and project them into the same interaction feature space.
Then we apply multi-layer GCN to combine the projected node-edge pairs to
capture the topological structures. Finally, the pooling representation of two
units is treated as the input of classifier to predict the probability. We
conduct offline experiments on two industrial datasets and one public dataset
and demonstrate that AGCN outperforms other excellent baselines. Moreover, we
also deploy AGCN method to important scenarios on Xianyu and AliExpress. In
online systems, AGCN achieves over 5% improvement on online metrics.
