Finding the Source of Information in Large Online Social Networks With Probabilistically Varying Internode Relationship Strengths
Jupyter Notebook
Rumour-Detection
Estimating the source of a rumor in large-scale social networks.
Points covered
Internode relationship strengths in real social networks are random.
We model this uncertainty by using random, non homogeneous edge weights on the underlying social network graph.
A novel two-stage algorithm that uses the modularity of the social network to locate the source of the rumor with fewer sensor nodes than other existing algorithms.
We also propose a novel method to select these sensor nodes.
We evaluate our algorithm using the data set of Karate Kid, Dolphin, and Football.
Simulations show that the proposed algorithm can determine the actual source within two hops, 69%–80% of the time when the diameter of the networks varies between 7 and 13.
Our numerical results also show that it is easier to estimate the source of a rumor when the source has higher betweenness centrality.
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
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