2021
DOI: 10.1063/5.0033197
|View full text |Cite
|
Sign up to set email alerts
|

Identifying influential nodes: A new method based on network efficiency of edge weight updating

Abstract: Identification of influential nodes in complex networks is an area of exciting growth since it can help us to deal with various problems. Furthermore, identifying important nodes can be used across various disciplines, such as disease, sociology, biology, engineering, just to name a few. Hence, how to identify influential nodes more accurately deserves further research. Traditional identification methods usually only focus on the local or global information of the network. However, only focusing on a part of t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 19 publications
(5 citation statements)
references
References 62 publications
0
5
0
Order By: Relevance
“…Meanwhile, in social networks, the frequency of interaction between nodes is also an important factor which reflects the mutual influence between their nodes. The higher the frequency of interaction, the higher the similarity between nodes [33]. Therefore, the frequency of inter-node interactions is high.…”
Section: Learning Engagement Based On Classroom Network Characteristicsmentioning
confidence: 99%
“…Meanwhile, in social networks, the frequency of interaction between nodes is also an important factor which reflects the mutual influence between their nodes. The higher the frequency of interaction, the higher the similarity between nodes [33]. Therefore, the frequency of inter-node interactions is high.…”
Section: Learning Engagement Based On Classroom Network Characteristicsmentioning
confidence: 99%
“…The igraph package (Csardi and Nepusz, 2006) was used for network construction. Regarding the network topology properties, we measured the relative importance of a network node in terms of the information centrality of the node, and used the ratio between the reduced value of the network efficiency after removing any node and the network efficiency of the network without removing any node as the information centrality of that arbitrary node, and we used the information centrality of the largest node in the network as the network vulnerability indicator (Shang et al, 2021). We used the glmer() function in the lme4 package to fit a generalized linear mixed-effect model (GLMM; Bates et al, 2015), with different treatments as random effects.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Complex network theory allows people to understand and attempt to solve a wide range of real-world systems, from technological networks [11] to social networks, including disease control, rumor transmission [12,13], biological systems [14,15], social systems [16][17][18], time series prediction [19], or information dissemination [20,21]. A complex system's special nodes may control a variety of the network's structural characteristics [22][23][24]. Terefore, the identifcation of infuential nodes is an important research direction in complex networks.…”
Section: Introductionmentioning
confidence: 99%
“…A weighted gravity model (WGC), based on this model, is used to evaluate the data. Shang et al [23] propose an improved gravity model that utilizes efective distance to capture dynamic information of the network and considers both local and global information of nodes. Considering the information and distance of neighboring nodes, Zhang et al [43] optimized the gravity model using the Laplace matrix.…”
Section: Introductionmentioning
confidence: 99%