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Finding Influential Spreaders from Human Activity beyond Network Location.

Min B, Liljeros F, Makse HA - PLoS ONE (2015)

Bottom Line: As a result, such metrics could be difficult to apply to real social networks.From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms.Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure.

View Article: PubMed Central - PubMed

Affiliation: Levich Institute and Physics Department, City College of New York, New York, NY, United States of America.

ABSTRACT
Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

No MeSH data available.


Related in: MedlinePlus

The effect of weak ties on spreading for different networks with diverse modularity.The panel shows the slope of the frequency of structural hole with respect to epidemic influence Mi in regression analysis as a function of modularity of a underlying network. For networks with highly modular structure such as LJ and PRO, the frequency of structural hole is positively correlated with Mi.
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pone.0136831.g003: The effect of weak ties on spreading for different networks with diverse modularity.The panel shows the slope of the frequency of structural hole with respect to epidemic influence Mi in regression analysis as a function of modularity of a underlying network. For networks with highly modular structure such as LJ and PRO, the frequency of structural hole is positively correlated with Mi.

Mentions: From the regression analysis, we confirm that people with high frequency of structural hole interaction is more likely to be an influential spreaders on LJ and PRO networks as the weak tie hypothesis. In LJ and PRO networks, the frequency of structural hole ash is positively related with the spreading influence Mi with extremely small p-value (Fig 3 and Tables I and J in S1 File). However, this pattern does not hold for all social networks that we tested. For QXF, QXG, and POK networks, is negatively correlated with Mi in contrary to the weak tie hypothesis (Fig 3 and Tables F-H in S1 File). This result suggests that the weak tie hypothesis may not be generically valid for all social networks.


Finding Influential Spreaders from Human Activity beyond Network Location.

Min B, Liljeros F, Makse HA - PLoS ONE (2015)

The effect of weak ties on spreading for different networks with diverse modularity.The panel shows the slope of the frequency of structural hole with respect to epidemic influence Mi in regression analysis as a function of modularity of a underlying network. For networks with highly modular structure such as LJ and PRO, the frequency of structural hole is positively correlated with Mi.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4554996&req=5

pone.0136831.g003: The effect of weak ties on spreading for different networks with diverse modularity.The panel shows the slope of the frequency of structural hole with respect to epidemic influence Mi in regression analysis as a function of modularity of a underlying network. For networks with highly modular structure such as LJ and PRO, the frequency of structural hole is positively correlated with Mi.
Mentions: From the regression analysis, we confirm that people with high frequency of structural hole interaction is more likely to be an influential spreaders on LJ and PRO networks as the weak tie hypothesis. In LJ and PRO networks, the frequency of structural hole ash is positively related with the spreading influence Mi with extremely small p-value (Fig 3 and Tables I and J in S1 File). However, this pattern does not hold for all social networks that we tested. For QXF, QXG, and POK networks, is negatively correlated with Mi in contrary to the weak tie hypothesis (Fig 3 and Tables F-H in S1 File). This result suggests that the weak tie hypothesis may not be generically valid for all social networks.

Bottom Line: As a result, such metrics could be difficult to apply to real social networks.From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms.Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure.

View Article: PubMed Central - PubMed

Affiliation: Levich Institute and Physics Department, City College of New York, New York, NY, United States of America.

ABSTRACT
Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

No MeSH data available.


Related in: MedlinePlus