activity
20182021
most citedIdentifying influential nodes based on fuzzy local dimension in complex networks

59 citations · 66 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20212 cited

SVBRDF Recovery From a Single Image With Highlights using a Pretrained Generative Adversarial Network

Tao Wen, Beibei Wang, Lei Zhang +2

Spatially-varying bi-directional reflectance distribution functions (SVBRDFs) are crucial for designers to incorporate new materials in virtual scenes, making them look more realis…

cs.SI2019

Evaluating the Vulnerability of Communities in Social Networks by Gravity Model

Tao Wen

With the development of network science, the various properties of complex networks have recently received extensive attention. Among these properties, the vulnerability of the com…

cs.SI20194 cited

Vital Spreaders Identification in Complex Networks with Multi-Local Dimension

Tao Wen, Danilo Pelusi, Yong Deng

The important nodes identification has been an interesting problem in this issue. Several centrality measures have been proposed to solve this problem, but most of previous methods…

cs.SI20191 cited

The vulnerability of communities in complex network: An entropy approach

Tao Wen, Yong Deng

Measuring the vulnerability of communities in complex network has become an important topic in the research of complex system. Numerous existing vulnerability measures have been pr…

cs.SI2019

Identification of influencers in complex networks by local information dimensionality

Tao Wen, Yong Deng

The identification of influential spreaders in complex networks is a popular topic in studies of network characteristics. Many centrality measures have been proposed to address thi…

cs.SI201859 cited

Identifying influential nodes based on fuzzy local dimension in complex networks

Tao Wen, Wen Jiang

How to identify influential nodes in complex networks is an important aspect in the study of complex network. In this paper, a novel fuzzy local dimension (FLD) is proposed to rank…