  Cross-platform account matching plays a significant role in social network
analytics, and is beneficial for a wide range of applications. However,
existing methods either heavily rely on high-quality user generated content
(including user profiles) or suffer from data insufficiency problem if only
focusing on network topology, which brings researchers into an insoluble
dilemma of model selection. In this paper, to address this problem, we propose
a novel framework that considers multi-level graph convolutions on both local
network structure and hypergraph structure in a unified manner. The proposed
method overcomes data insufficiency problem of existing work and does not
necessarily rely on user demographic information. Moreover, to adapt the
proposed method to be capable of handling large-scale social networks, we
propose a two-phase space reconciliation mechanism to align the embedding
spaces in both network partitioning based parallel training and account
matching across different social networks. Extensive experiments have been
conducted on two large-scale real-life social networks. The experimental
results demonstrate that the proposed method outperforms the state-of-the-art
models with a big margin.
