GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph
Convolutional Networks ?
Abstract
Graph Convolutional Networks (GCNs) have
proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and
node features are successfully leveraged in existing GCNs, however, attributes that graph links may
carry are commonly ignored, as almost all of these
models simplify graph links into binary or scalar
values describing node connectedness. In our paper instead, links are reverted to hypostatic relationships between entities with descriptional attributes. We propose GCN-LASE (GCN with Link
Attributes and Sampling Estimation), a novel GCN
model taking both node and link attributes as inputs. To adequately captures the interactions between link and node attributes, their tensor product is used as neighbor features, based on which
we define several graph kernels and further develop according architectures for LASE. Besides,
to accelerate the training process, the sum of features in entire neighborhoods are estimated through
Monte Carlo method, with novel sampling strategies designed for LASE to minimize the estimation
variance. Our experiments show that LASE outperforms strong baselines over various graph datasets,
and further experiments corroborate the informativeness of link attributes and our model’s ability
of adequately leveraging them.