Nonlinear Structural Fusion for Multiplex Network

3Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection.

References Powered by Scopus

Community structure in social and biological networks

12299Citations
N/AReaders
Get full text

Node2vec: Scalable feature learning for networks

8672Citations
N/AReaders
Get full text

Multilayer networks

2482Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Network representation learning via improved random walk with restart

12Citations
N/AReaders
Get full text

Multiplex Network Embedding Model with High-Order Node Dependence

6Citations
N/AReaders
Get full text

SAME: Sampling Attack in Multiplex Network Embedding

0Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Ning, N., Long, F., Wang, C., Zhang, Y., Yang, Y., & Wu, B. (2020). Nonlinear Structural Fusion for Multiplex Network. Complexity, 2020. https://doi.org/10.1155/2020/7041564

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 4

80%

Researcher 1

20%

Readers' Discipline

Tooltip

Computer Science 2

50%

Business, Management and Accounting 1

25%

Mathematics 1

25%

Save time finding and organizing research with Mendeley

Sign up for free