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DeHIN A Decentralized Framework for Embedding Large-Scale Heterogeneous Information Networks.pdf
- 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.