Uncovering information diffusion patterns in different networks using the L-metric

ABSTRACT

Information diffusion is an important branch of online social network analysis. In this paper, we construct a new metric, the proportion of leaf nodes in a diffusion tree (L-metric), to quantify information diffusion patterns, and we study the impact of the network category and information content on these patterns. Simulation-based experimental studies of real-world social networks show that information diffusion exhibits different patterns in different networks, and niche information does not typically propagate easily in any type of network. These conclusions provide a new perspective for further research on management decisions with regard to online social networks.

KEYWORDS:

• Diffusion pattern
• social network
• information diffusion

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes

1. https://www.aminer.cn/data/?nav=openData

2. https://www.aminer.cn/heterinf

3. https://www.aminer.cn/heterinf

4. https://grouplens.org/datasets/hetrec-2011/

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under grant numbers [71901083, 71932005, and 71673074] and by Zhejiang Laboratory under grant number [2019KE0AB04].