Identifying influential spreaders by weighted LeaderRank

Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank (Lü et al., 2011). According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders; (ii) the higher tolerance to noisy data; and (iii) the higher robustness to intentional attacks

Locating influential nodes via dynamics-sensitive centrality

With great theoretical and practical significance, locating influential nodes of complex networks is a promising issues. In this paper, we propose a dynamics-sensitive (DS) centrality that integrates topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is much more accurate than degree, $k$-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure

A New Ranking Technique to Enhance the Infection Size in Complex Networks

Detecting the spreaders/sources in complex networks is an essential manner to understand the dynamics of the information spreading process. Consider the k-Shell centrality metric, which is taken into account the structural position of a node within the network, a more effective metric in picking the node which has more ability on spreading the infection compared to other centrality metrics such the degree, between and closeness.  However, the K-Shell method suffers from some boundaries, it gives the same K-Shell index to a lot of the nodes, and it uses only one indicator to rank the nodes. A new technique is proposed in this research to develop the K-Shell metric by using the degree of the node, and a coreness of its rounding friends to estimate the ability of the node in spreading the infection within the network. The experimental results, which were done on four types of real and synthetic networks, and using an epidemic propagation model SIR, demonstrate that the suggested technique can measure the node effect more precisely and offer a unique ordering group than other centrality measures

Predicting Item Popularity: Analysing Local Clustering Behaviour of Users

Predicting the popularity of items in rating networks is an interesting but challenging problem. This is especially so when an item has first appeared and has received very few ratings. In this paper, we propose a novel approach to predicting the future popularity of new items in rating networks, defining a new bipartite clustering coefficient to predict the popularity of movies and stories in the MovieLens and Digg networks respectively. We show that the clustering behaviour of the first user who rates a new item gives insight into the future popularity of that item. Our method predicts, with a success rate of over 65% for the MovieLens network and over 50% for the Digg network, the future popularity of an item. This is a major improvement on current results.Comment: 25 pages, 11 figure

Rumors and Rumor Corrections on Twitter : Studying Message Characteristics and Opinion Leadership

As rumors often ripple across the cyberspace, posting rumor corrections on social media can bring about social good by spreading the truth. However, rumors and rumor corrections are not easily distinguishable from one another. Therefore, this paper investigates how three message characteristics, namely, the use of emotions, clarity and credible source attribution, can predict message veracity on social media. Message veracity denotes whether a message is a rumor or a rumor correction. In addition, the paper further examines the extent to which opinion leadership moderates the relation between message characteristics and message veracity. Set against the context of the death hoax of Singapore’s first Prime Minister Lee Kuan Yew in March 2015, data for this paper came from Twitter. Analysis involved binary logistic regression. All the three message characteristics predicted veracity. Rumor corrections were characterized by lower use of emotions, higher clarity, and higher credible source attribution compared with rumors. Furthermore, opinion leadership moderated the relation between the use of emotions and message veracity as well as that between credible source attribution and message veracity

Identifying influencers from sampled social networks

Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%–30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks