Hyper2vec: Biased Random Walk for Hyper-network Embedding

Jie Huang, Chuan Chen, Fanghua Ye, Jiajing Wu, Zibin Zheng, Guohui Ling

Abstract: Network embedding aims to obtain a low-dimensional representation of vertices in a network, meanwhile preserving structural and inherent properties of the network. Recently, there has been growing interest in this topic while most of the existing network embedding models mainly focus on normal networks in which there are only pairwise relationships between the vertices. However, in many realistic situations, the relationships between the objects are not pairwise and can be better modeled by a hyper-network in which each edge can join an uncertain number of vertices. In this paper, we propose a deep model called Hyper2vec to learn the embeddings of hyper-networks. Our model applies a biased $$2^{nd}$$ order random walk strategy to hyper-networks in the framework of Skip-gram, which can be flexibly applied to various types of hyper-networks.