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Benchmarking Measures of Network Influence

View Article: PubMed Central - PubMed

ABSTRACT

Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.

No MeSH data available.


Plot of TKO scores across time for SIR dynamics and a scalefree network.These examples show that the most influential agent-times often do not occur during the initial phases of a disease, but can indicate bottlenecks in the spread of the disease. This also shows the appearance of negative TKO agents, the removal of which actually increases the morbidity of the disease due to timing and network effects.
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f2: Plot of TKO scores across time for SIR dynamics and a scalefree network.These examples show that the most influential agent-times often do not occur during the initial phases of a disease, but can indicate bottlenecks in the spread of the disease. This also shows the appearance of negative TKO agents, the removal of which actually increases the morbidity of the disease due to timing and network effects.

Mentions: The poor correlations between TKO and both agent-initiated magnitude and epidemic probability have multiple explanations. To understand the relationship better we present a few select plots of the agent TKO scores across time in Fig. 2. These plots present the change in magnitude resulting from removing each infectious agent at each time averaged across the 200 runs initialized with each agent being infected. So a value of m means that on average (i.e., regardless of which agent is initially infected) removing this agent at this time decreases morbidity by m agent-times.


Benchmarking Measures of Network Influence
Plot of TKO scores across time for SIR dynamics and a scalefree network.These examples show that the most influential agent-times often do not occur during the initial phases of a disease, but can indicate bottlenecks in the spread of the disease. This also shows the appearance of negative TKO agents, the removal of which actually increases the morbidity of the disease due to timing and network effects.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037445&req=5

f2: Plot of TKO scores across time for SIR dynamics and a scalefree network.These examples show that the most influential agent-times often do not occur during the initial phases of a disease, but can indicate bottlenecks in the spread of the disease. This also shows the appearance of negative TKO agents, the removal of which actually increases the morbidity of the disease due to timing and network effects.
Mentions: The poor correlations between TKO and both agent-initiated magnitude and epidemic probability have multiple explanations. To understand the relationship better we present a few select plots of the agent TKO scores across time in Fig. 2. These plots present the change in magnitude resulting from removing each infectious agent at each time averaged across the 200 runs initialized with each agent being infected. So a value of m means that on average (i.e., regardless of which agent is initially infected) removing this agent at this time decreases morbidity by m agent-times.

View Article: PubMed Central - PubMed

ABSTRACT

Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and k-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that this TKO score is an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.

No MeSH data available.