  Precisely understanding the business relationships between Autonomous Systems
(ASes) is essential for studying the Internet structure. So far, many inference
algorithms have been proposed to classify the AS relationships, which mainly
focus on Peer-Peer (P2P) and Provider-Customer (P2C) binary classification and
achieved excellent results. However, there are other types of AS relationships
in actual scenarios, i.e., the businessbased sibling and structure-based
exchange relationships, that were neglected in the previous research. These
relationships are usually difficult to be inferred by existing algorithms
because there is no discrimination on the designed features compared to the P2P
or P2C relationships.
  In this paper, we focus on the multi-classification of AS relationships for
the first time. We first summarize the differences between AS relationships
under the structural and attribute features, and the reasons why multiple
relationships are difficult to be inferred. We then introduce new features and
propose a Graph Convolutional Network (GCN) framework, AS-GCN, to solve this
multi-classification problem under complex scene. The framework takes into
account the global network structure and local link features concurrently. The
experiments on real Internet topological data validate the effectiveness of our
method, i.e., AS-GCN achieves comparable results on the easy binary
classification task, and outperforms a series of baselines on the more
difficult multi-classification task, with the overall accuracy above 95%.
