Predicting stimulation index of information transmissions by local structural features in social networks
Bibliographic Data
| ID | 16858931 |
|---|---|
| Authors | Kazufumi Inafuku (0000-0002-3122-3771, University of Tsukuba, corresponding author), Takayasu Fushimi (0000-0003-3448-8182, Tokyo University of Technology), Tetsuji Satoh (University of Tsukuba) |
| Year | 2022 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2022-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-022-00865-0 |
| OpenAlex | W4214663430 |
| Language | EN |
| References cited | 32 |
Complex network · Computer network · Data mining · Diffusion · Enhanced Data Rates for GSM Evolution · Information transmission · Social media · Telecommunications · World Wide Web · Complex Network Analysis Techniques · Computer Science · Mental Health Research Topics · Opinion Dynamics and Social Influence · Artificial Intelligence
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| Highly cited | No |
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