Graph Convolutional Networks on User Mobility Heterogeneous Graphs
for Social Relationship Inference
Abstract
Inferring social relations from user trajectory data
is of great value in real-world applications such as
friend recommendation and ride-sharing. Most existing methods predict relationship based on a pairwise approach using some hand-crafted features or
rely on a simple skip-gram based model to learn
embeddings on graphs. Using hand-crafted features often fails to capture the complex dynamics in human social relations, while the graph embedding based methods only use random walks
to propagate information and cannot incorporate
external semantic data provided. We propose a
novel model that utilizes Graph Convolutional Networks (GCNs) to learn user embeddings on the
User Mobility Heterogeneous Graph in an unsupervised manner. This model is capable of propagating relation layer-wisely as well as combining both
the rich structural information in the heterogeneous
graph and predictive node features provided. Our
method can also be extended to a semi-supervised
setting if a part of the social network is available.
The evaluation on three real-world datasets demonstrates that our method outperforms the state-ofthe-art approaches