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1. DeHIN: A Decentralized Framework for
Embedding Large
Information Networks
Abstract
Modeling heterogeneity by extraction and exploitation of high
information from heterogeneous information networks (HINs) has been
attracting immense research attention in recent times. Such heterogeneous
network embedding (HNE) methods effectively ha
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
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
effectiveness guarantee, we present Decen
DeHIN: A Decentralized Framework for
Embedding Large-Scale Heterogeneous
Information Networks
Modeling heterogeneity by extraction and exploitation of high
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
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
s 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 Decentralized Embedding Framework for
DeHIN: A Decentralized Framework for
Scale Heterogeneous
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
rness the heterogeneity of
scale HINs. However, in the real world, the size of HINs grow
exponentially with the continuous introduction of new nodes and different
scale network. Learning node embeddings on
s creates a performance bottleneck for existing HNE methods that
are commonly centralized, i.e., complete data and the model are both on a
scale HNE tasks with strong efficiency and
tralized Embedding Framework for

2. 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.
As shown from our experimental results, DeHIN significantly improves the
efficiency and accuracy of existing HNE models as well as outperforms the
large-scale graph embedding frameworks by efficiently scaling up to large-
scale HINs.