.png){kind=icon}
Return
A Hyperbolic Embedding Method for Weighted Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TNSE.2020.3046746.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Network embedding, which is the task of learning low-dimensional representations of vertices, has attracted increasing attention recently. Evidences have been found that the hidden metric space of many realistic complex networks is hyperbolic. The topology and weight emerge naturally as reflections of the hyperbolic metric property. A common objective of hyperbolic embedding is to maximize the likelihood function of the hyperbolic network model. The difficulty is that the likelihood function is non-concave which is difficult to optimize. In this paper, we propose a hyperbolic embedding method for weighted networks. To prevent the optimization from falling into numerous local optima, initial embedding is obtained by approximation. A proposed gradient algorithm then improves the embedding according to the likelihood function. Experiments on synthetic and real networks show that the proposed method achieves good embedding performance with respect to different quality metrics and applications.
Keywords:
Measurement
Geometry
Complex networks
Approximation algorithms
Topology
Task analysis
Probability density function
Hyperbolic space
network embedding
weighted complex network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
7.9
Papers:
2.6K
Citations:
10.0K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveInsurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
OAAIShareSaveShareSave
View more