The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Blogosphere: research issues, tools, and applications
ACM SIGKDD Explorations Newsletter
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
A note on maximizing the spread of influence in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Which targets to contact first to maximize influence over social network
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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Estimating influence of a node is an important problem in social network analyses. We address this problem in a particular class of model for opinion propagation in which a node adopts its opinion based on not only its direct neighbors but also the average opinion share over the whole network, which we call an extended Voter Model with uniform adoption (VM). We found a similarity of this model with the well known PageRank (PR) and explored the relationships between the two. Since the uniform adoption implies the random opinion adoption of all nodes in the network, it corresponds to the random surfer jump of PR. For an undirected network, both VM and PR give the same ranking score vector because the adjacency matrix is symmetric, but for a directed network, the score vector is different for both because the adjacency matrix is asymmetric. We investigated the effect of the uniform adoption probability on ranking and how the ranking correlation between VM and PR changes using four real world social networks. The results indicate that there is little correlation between VM and PR when the uniform adoption probability is small but the correlation becomes larger when both the uniform adoption and the random surfer jump probabilities become larger. We identified that the recommended value for the uniform adoption probability is to be around 0.25 to obtain a stable solution.