scholarly journals Efficient Parallel Simulation over Large-scale Social Contact Networks

2019 ◽  
Vol 29 (2) ◽  
pp. 1-25
Author(s):  
Yulin Wu ◽  
Wentong Cai ◽  
Zengxiang Li ◽  
Wen Jun Tan ◽  
Xiangting Hou
Keyword(s):  
Large Scale ◽  
Social Contact ◽  
Parallel Simulation ◽  
Contact Networks ◽  
Social Contact Networks

scholarly journals Tracking social contact networks with online respondent-driven detection: who recruits whom?

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Mart L. Stein ◽  
Peter G. M. van der Heijden ◽  
Vincent Buskens ◽  
Jim E. van Steenbergen ◽  
Linus Bengtsson ◽  
...  
Keyword(s):  
Social Contact ◽  
Contact Networks ◽  
Social Contact Networks

Generation and analysis of large synthetic social contact networks

2009 ◽  
Author(s):  
Christopher L. Barrett ◽  
Richard J. Beckman ◽  
Maleq Khan ◽  
V. S. Anil Kumar ◽  
Madhav V. Marathe ◽  
...  
Keyword(s):  
Social Contact ◽  
Contact Networks ◽  
Social Contact Networks

scholarly journals Modeling Epidemics Spreading on Social Contact Networks

2015 ◽  
Vol 3 (3) ◽  
pp. 410-419 ◽  
Author(s):  
Zhaoyang Zhang ◽  
Honggang Wang ◽  
Chonggang Wang ◽  
Hua Fang
Keyword(s):  
Social Contact ◽  
Contact Networks ◽  
Social Contact Networks

Identifying important nodes based on upstream and downstream time-respecting paths in temporal networks

2021 ◽  
pp. 2150403
Author(s):  
Liang Luo ◽  
Minghao Li ◽  
Zili Zhang ◽  
Li Tao
Keyword(s):  
Real World ◽  
Social Contact ◽  
Centrality Measures ◽  
Temporal Networks ◽  
Contact Networks ◽  
Node Importance ◽  
Comparison Results ◽  
Temporal Structures ◽  
Social Contact Networks ◽  
The One

Identifying the nodes that play significant roles in the epidemic spreading process has attracted extensive attention in recent years. Few centrality measures, such as temporal degree and temporal closeness centrality, have been proposed to quantify node importance based on the topological structure of social contact networks. Most methods estimate the importance of a node from a single aspect, e.g. a higher degree in time snapshot graphs, or shorter distances to other nodes along time-respecting paths. However, this may not be the case in the real world. On the one hand, a node with more nodes on its out streams (i.e. downstream) should be more important because it may affect more nodes along its time-stamped contacting paths once it is infected. On the other hand, a node with more nodes in its in streams (i.e. upstream) deserves closer attention, as it has a higher probability of infection by other nodes. We propose a new temporal centrality measure, upstream and downstream centrality (UD-centrality) with two forms of realizations, i.e. a linear UD-centrality (L-UD) and a product UD-centrality (P-UD) to estimate the importance of nodes based on the temporal structures of social contact networks. We compare our L-UD and P-UD to three classic temporal network centralities through simulations on 14 real-world temporal networks based on the susceptible-infected (SI) model. The comparison results show that UD-centrality can more accurately rank the importance of nodes than the baseline centrality measures.

scholarly journals Structure and consistency of self-reported social contact networks in British secondary schools

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0200090 ◽  
Author(s):  
Adam J. Kucharski ◽  
Clare Wenham ◽  
Polly Brownlee ◽  
Lucie Racon ◽  
Natasha Widmer ◽  
...  
Keyword(s):  
Secondary Schools ◽  
Social Contact ◽  
Contact Networks ◽  
Social Contact Networks
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