# A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning

arXiv: https://arxiv.org/abs/2007.11380 · HTML: https://mlsanity.com/paper/2007.11380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://mlsanity.com/paper/2203.07687/) (2022)

## Abstract (arXiv)

Network Embedding has been widely studied to model and manage data in a variety of real-world applications. However, most existing works focus on networks with single-typed nodes or edges, with limited consideration of unbalanced distributions of nodes and edges. In real-world applications, networks usually consist of billions of various types of nodes and edges with abundant attributes. To tackle these challenges, in this paper we propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning. Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions and propose a unified framework for the embedding learning. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba. The results empirically demonstrate that MSM can achieve relatively significant gains over previous state-of-arts on link prediction.