The Tipping Point: How Little Things Make a Big Difference

Jackson, Rudolph S.

doi:10.1080/15210960701386491

Cited by 17 publications

(1 citation statement)

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“…[23] Elite users like media, celebrities, bloggers, and organizations roughly are 0.05% of the population and account of about 50% attention. [24,25] Other personal information like age, gender, location, and so on is less mentioned. Thus identity (e.g., celebrities, actors, writers), role (e.g., connectors, experts, and salesman) and interest (e.g., favorite book, music, and so on) are regarded as some of the more important node attributes in social networks.…”

Section: Evaluation Of Node Heterogeneitymentioning

confidence: 99%

Identifying influential nodes based on graph signal processing in complex networks

Zhao

et al. 2015

Chinese Phys. B

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Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity (i.e., different attributes such as interest, energy, age, and so on) ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal processing based centrality (GSPC) method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

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Section: Evaluation Of Node Heterogeneitymentioning

confidence: 99%