As we all know, online social networks are a key component of everyday life. More and more of our interactions with others are taking place online through social media sites such as Instagram and Twitter. Al-garadi, Varathan, and Ravana (2017), sought to improve the existing K-core method for online social networks by using a link-weighing method based on interactions among individuals. The purpose of the study was to identify the influential spreaders in online social networks more accurately in comparison to other methods such as degree centrality and PageRank.
The study sample consisted of two large online social networks from Twitter. The dataset for the first network came before, during , and after the announcement of the discovery of a new particle. The first dataset was made up of three data of the same user IDs: the social network, the retweet network, and the mention network. The social network accounted for the social structure while the retweet and mention networks were used to weigh the social network (Al-garadi et al., 2017). The second dataset consisted of 121,807,378 tweets that were posted by 14,599,240 different users. The second dataset was used to create an undirected, unweighted social network.
The nodes in this network were the individual Twitter user IDs. The links were the connections (follows) between the individual user IDs that were then weighted using the retweet and mention networks.
In order to determine which method was the most accurate, the study calculated the imprecision functions of the other methods used to determine influential spreaders in a network. Imprecision function values that were close to 0 are indicative of high diffusion efficiency given that the users selected are nearly those that provide the most information dissemination (Al-garadi et al., 2017). The study found that using a weighed K-core decomposition method was the most accurate in identifying the most influential spreaders in the network. This method can be used in the future to propose a new model for the spread of information in online social networks as well as lead to the creation of new nerve ration of information spreading models (Al-garadi et al., 2017). In my own field of criminal justice, this method could be used to identify the influential spreaders within a criminal network in order to target them to hinder the spread of information through the criminal network.
Al-garadi, M.A., Varathan, K.D., Ravana, S.D. (2017. Identification of influential spreaders in online social networks using interaction weighted k-core decomposition method. Physica A: Statistical Mechanics and its Applications, 468, 278–288