Xiaoke Xu
Biographic Data
| ID | 3863698 |
|---|---|
| NAME | Xiaoke Xu |
| GIVEN NAMES | Xiaoke |
| FAMILY NAME | Xu |
| SIGNATURE | XU X |
| AFFILIATIONS | Beijing Normal University |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Hypergraph Community Detection Based on Higher-Order Topology and Information Flow
Hypergraph community detection has gained significant attention due to reflecting higher-order mesoscale structures and facilitating functional analysis. However, previous methods mainly rely on a single and biased hypergraph property, i.e., static hyperedge structures or dynamic information flow, but largely overlook their intrinsic cooccurrence and potential conflicts in real-world situations. To address this issue, we first model hypergraph co…
Beyond the Targeted Customer: Spillover Effect through Social Influence
Following an innovative large-scale field experiment, this study measures the extent of the spillover effect through social influence in a social advertising campaign. Results show that, even when the campaign does not have a significant direct effect on targeted customers, it still creates a significant spillover effect on targets’ peers because of social influence. The spillover effect is achieved through weak ties (acquaintances) rather than s…
Multiple bursts of highly retweeted articles on social media
Low dispersion in the infectiousness of Covid-19 cases implies difficulty in control
The individual infectiousness of coronavirus disease 2019 (COVID-19), quantified by the number of secondary cases of a typical index case, is conventionally modelled by a negative-binomial (NB) distribution. Based on patient data of 9120 confirmed cases in China, we calculated the variation of the individual infectiousness, i.e., the dispersion parameter k of the NB distribution, at 0.70 (95% confidence interval: 0.59, 0.98). This suggests that t…
Realistic modelling of information spread using peer-to-peer diffusion patterns
Low dispersion in the infectiousness of Covid-19 cases implies difficulty in control
The individual infectiousness of coronavirus disease 2019 (COVID-19), quantified by the number of secondary cases of a typical index case, is conventionally modelled by a negative-binomial (NB) distribution. Based on patient data of 9120 confirmed cases in China, we calculated the variation of the individual infectiousness, i.e., the dispersion parameter k of the NB distribution, at 0.70 (95% confidence interval: 0.59, 0.98). This suggests that t…
Realistic modelling of information spread using peer-to-peer diffusion patterns
Multiple bursts of highly retweeted articles on social media
Beyond the Targeted Customer: Spillover Effect through Social Influence
Following an innovative large-scale field experiment, this study measures the extent of the spillover effect through social influence in a social advertising campaign. Results show that, even when the campaign does not have a significant direct effect on targeted customers, it still creates a significant spillover effect on targets’ peers because of social influence. The spillover effect is achieved through weak ties (acquaintances) rather than s…
Hypergraph Community Detection Based on Higher-Order Topology and Information Flow
Hypergraph community detection has gained significant attention due to reflecting higher-order mesoscale structures and facilitating functional analysis. However, previous methods mainly rely on a single and biased hypergraph property, i.e., static hyperedge structures or dynamic information flow, but largely overlook their intrinsic cooccurrence and potential conflicts in real-world situations. To address this issue, we first model hypergraph co…
Complex Network Analysis Techniques (3 works) · Mathematics (3 works) · Opinion Dynamics and Social Influence (3 works) · Computer Science (2 works) · Diffusion (2 works) · Statistics (2 works) · 2019-20 coronavirus outbreak (1 works) · Advanced Graph Neural Networks (1 works) · Advertising (1 works) · Artificial Intelligence (1 works)