Efficient link cuts in online social networks

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Junjun Ruan (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/
Jing Deng

Abstract: Due to the huge popularity of online social networks, many researchers focus on adding links, e.g., link prediction to help friend recommendation. So far, no research has been performed on link cuts. However, the spread of malware and misinformation can cause havoc and hence it is interesting to see how to cut links such that malware and misinformation will not run rampant. In fact, many online social networks can be modelled as undirected graphs with nodes represents users and edges stands for relationships between users. In this paper, we investigate different strategies to cut links among different users in undirected graphs so that the speed of virus and misinformation spread can be slowed down the most or even cut off. Our algorithm is very flexible and can be applied to other networks. For example, it can be applied to email networks to stop the spread of viruses and spam emails; it can also be used in neural networks to stop the diffusion of worms and diseases. Two measures are chosen to evaluate the performance of these strategies: Average Inverse of Shortest Path Length (AIPL) and Rumor Saturation Rate (RSR). AIPL measures the communication efficiency of the whole graph while RSR checks the percentage of users receiving information within a certain time interval. Compared to AIPL, RSR is an even better measure as it concentrates on some specific rumors' spread in online networks. Our experiments are performed on both synthetic data and Facebook data. According to the evaluation on the two measures, it turns out that our algorithm performs better than random cuts and different strategies can have better performance in their suitable situations.

Additional Information

Language: English
Date: 2015
Link Cuts, Rumor, Social Networks
Online social networks $x Mathematical models
Online social networks $x Computer simulation
Online social networks $x Security measures
Social media $x Security measures
Internet $x Security measures
Computer security

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