Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2021

Abstract

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.

Keywords

Dimensionality reduction, Heterogeneous network, Representation learning

Discipline

Databases and Information Systems | Data Science | OS and Networks

Research Areas

Data Science and Engineering

Publication

Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17: Proceedings

Volume

12976

First Page

399

Last Page

416

ISBN

9783030865191

Identifier

10.1007/978-3-030-86520-7_25

Publisher

Springer

City or Country

Cham

Embargo Period

12-13-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-86520-7_25

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