Hash Embedding for Attributed Multiplex Heterogeneous Network

Heterogeneous networks have been widely utilized in many fields. However, existing network embedding methods often meet challenges when dealing with heterogeneous networks. One of them is the underutilization of node attribute information, resulting in a lack of representational power. Another challenge is the complexity of the network structure, which makes existing representations often unable to capture important features of the network, thus affecting the effect of downstream tasks. The hash embedding for attributed multiplex heterogeneous network (AMHEN) aims to solve the above problems. By integrating the attributes of nodes and the network structure information into the node embedding, the method uses the deep hash layer to learn the compact representation of nodes, so as to obtain the hash embedding. Compared with the traditional embedding method, the proposed method can better retain the important attributes of nodes, and compress the node representation into fixed length binary coding by hash technology, which improves the efficiency and scalability of the embedding. Sufficient experimental results show that the proposed hash embedding for AMHEN can significantly reduce the embedding dimension while maintaining the embedding quality, thus providing a more efficient network embedding for subsequent downstream tasks. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.