In this paper, we propose a method of ranking recently created Twitter accounts according to their prospective popularity. Early detection of new promising accounts is useful for trend prediction, viral marketing, user recommendation, and so on. New accounts are, however, difficult to evaluate because they have not established their reputations, and we cannot apply existing link-based or other popularity-based account evaluation methods. Our method first finds "early adopters", i.e., users who often find new good information sources earlier than others. Our method then regards new accounts followed by good early adopters as promising, even if they do not have many followers now. In order to find good early adopters, we estimate the frequency of link propagation from each account, i.e., how many times the follow links from the account have been copied by its followers. If its followers have copied many of its follow links in the past, the account must be an early adopter, who find good information sources earlier than its followers. We develop a method of inferring which links are created by copying which links. One advantage of our method is that our method only uses information that can be easily obtained only by crawling neighbors of the target accounts in the current Twitter graph. We evaluated our method by an experiment on Twitter data. We chose then-new accounts from an old snapshot of Twitter, compute their ranking by our method, and compare it with the number of followers the accounts currently have. The result shows that our method produces better rankings than various baseline methods, especially for new accounts that have only a few followers.
Recently, there have also been many studies on link prediction in online social network. For example, Liben-Nowell and Kleinberg @cite_2 was the first to formulate the problem of link-prediction on social network and they proposed a prediction method based on the proximity of nodes in the network. @cite_10 proposed a method that estimates the probability of future links by inferring latent paths of link propagation in the network. They estimate how important each node is as a mediator of link propagation by using a probabilistic model. Our method is based on a similar concept of early adopters. Their method, however, requires multiple snapshots of the network structure at different time point. On the other hand, our method for estimating the future popularity of a given account only requires information that can be obtained by crawling the neighbors of the target account in the current snapshot of the network structure. This is one big advantage of our method.
@cite_10 @cite_2
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It's well known that the transitivity of friendship is a popular sociological principle in social networks. However, it's still unknown that to what extent people's friend-making behaviors follow this principle and to what extent it can benefit the link prediction task. In this paper, we try to adopt this sociological principle to explain the evolution of networks and study the latent friendship propagation. Unlike traditional link prediction approaches, we model link formation as results of individuals' friend-making behaviors combined with personal interests. We propose the Latent Friendship Propagation Network (LFPN), which depicts the evolution progress of one's egocentric network and reveals future growth potentials driven by the transitivity of friendship based on personal interests. We model individuals' social behaviors using the Latent Friendship Propagation Model (LFPM), a probabilistic generative model from which the LFPN can be learned effectively. To evaluate the power of the friendship propagation in link prediction, we design LFPN-RW which models the friend-making behavior as a random walk upon the LFPN naturally and captures the co-influence effect of the friend circles as well as personal interests to provide more accurate prediction. Experimental results on real-world datasets show that LFPN-RW outperforms the state-of-the-art approaches. This convinces that the transitivity of friendship actually plays important roles in the evolution of social networks. Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. © 2007 Wiley Periodicals, Inc.