  Modeling heterogeneity by extraction and exploitation of high-order
information from heterogeneous information networks (HINs) has been attracting
immense research attention in recent times. Such heterogeneous network
embedding (HNE) methods effectively harness the heterogeneity of small-scale
HINs. However, in the real world, the size of HINs grow exponentially with the
continuous introduction of new nodes and different types of links, making it a
billion-scale network. Learning node embeddings on such HINs creates a
performance bottleneck for existing HNE methods that are commonly centralized,
i.e., complete data and the model are both on a single machine. To address
large-scale HNE tasks with strong efficiency and effectiveness guarantee, we
present \textit{Decentralized Embedding Framework for Heterogeneous Information
Network} (DeHIN) in this paper. In DeHIN, we generate a distributed parallel
pipeline that utilizes hypergraphs in order to infuse parallelization into the
HNE task. DeHIN presents a context preserving partition mechanism that
innovatively formulates a large HIN as a hypergraph, whose hyperedges connect
semantically similar nodes. Our framework then adopts a decentralized strategy
to efficiently partition HINs by adopting a tree-like pipeline. Then, each
resulting subnetwork is assigned to a distributed worker, which employs the
deep information maximization theorem to locally learn node embeddings from the
partition it receives. We further devise a novel embedding alignment scheme to
precisely project independently learned node embeddings from all subnetworks
onto a common vector space, thus allowing for downstream tasks like link
prediction and node classification.
