Representation learning on multi-layered heterogeneous network

• Main Authors: ZHANG, Delvin Ce, LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Dimensionality reduction; Heterogeneous network; Representation learning; Databases and Information Systems; Data Science; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6433; https://ink.library.smu.edu.sg/context/sis_research/article/7436/viewcontent/ecmlpkdd21a.pdf
• Institution: Singapore Management University

Network data can often be represented in a multi-layered structure with rich semantics. One example is e-commerce data, containing user-user social network layer and item-item context layer, with cross-layer user-item interactions. Given the dual characters of homogeneity within each layer and heterogeneity across layers, we seek to learn node representations from such a multi-layered heterogeneous network while jointly preserving structural information and network semantics. In contrast, previous works on network embedding mainly focus on single-layered or homogeneous networks with one type of nodes and links. In this paper we propose intra- and cross-layer proximity concepts. Intra-layer proximity simulates propagation along homogeneous nodes to explore latent structural similarities. Cross-layer proximity captures network semantics by extending heterogeneous neighborhood across layers. Through extensive experiments on four datasets, we demonstrate that our model achieves substantial gains in different real-world domains over state-of-the-art baselines.