Searching for superspreaders of information in real-world social media

Searching for superspreaders of information in real-world social mediaOverview and Major FindingsBackgroundGeneral approach taken in this studyaverage influencerecognition rateResultsLocal Proxy

Overview and Major Findings





General approach taken in this study

average influence

The average influence, , is calculated for nodes with a given combination of (k-core) and (in-degree) as:


recognition rate

The recognition rate is defined as

Where and are sets of nodes ranking in the top fraction by influence and predictor, respectively, and is the number of nodes in .



They illustrate a number of different ways that k-core does the best for all networks in pretty much all ways. I simply paste the figures and their descriptions below to illustrate the results.






Local Proxy

Since k-core is a global measure it's usefulness is quite limited — rarely to we have a full picture of all the connections within a social network.

Thus, the authors offer a local proxy which seeks to stand in for k-core by utilizing only partial network information.

Because k-core appears to not only take into account the degree of an individual node but also the degree of those around it, the authors create and test two simple metrics:

  1. — Node 's is the sum of the degree of all the nearest neighbors of node .
  2. — Is the same as except it now accounts for the nearest-neighbors of node 's nearest neighbors. Thus, this measure takes the sum of node 's nearest neighbor as well as the next nearest neighbor.

We can see below that performs on-par with k-core and doesn't add much additional value.

Note: While this would certainly require much less Twitter data than reconstructing the entire network, gathering this data on just the nearest neighbors for even a small sample of Twitter users (say a few hundred) is still extremely temporally expensive and can require days to simply gather that data.