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Link injection for boosting information spread in social networks

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Abstract

Social media have become popular platforms for spreading information. Several applications, such as ‘viral marketing’, pause the requirement for attaining large-scale information spread in the form of word-of-mouth that reaches a large number of users. In this paper, we propose a novel method that predicts new social links that can be inserted among existing users of a social network, aiming directly at boosting information spread and increasing its reach. We refer to this task as ‘link injection’, because unlike most existing people-recommendation methods, it focuses directly on information spread. A set of candidate links for injection is first predicted in a collaborative-filtering fashion, which generates personalized candidate connections. We select among the candidate links a constrained number that will be finally injected based on a novel application of a score that measures the importance of nodes in a social graph, following the strategy of injecting links adjacent to the most important nodes. The proposed method is suitable for real-world applications, because the injected links manage to substantially increase the reach of information spread by controlling at the same time the number of injected links not to affect the user experience. We evaluate the performance of our proposed methodology by examining several real data sets from social networks under several distinct factors. The experimentation demonstrates the effectiveness of our proposed method, which increases the spread by more than a twofold factor by injecting as few as half of the existing number of links.

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Notes

  1. We have to note that the focus of Tong et al. (2010) is to identify which nodes should be removed from a network to make it more robust against epidemic spread. In contrast, our goal is to identify the nodes that make the graph more susceptible to the (viral) spread of information when injecting new links adjacent to these nodes.

  2. At the beginning of the algorithm, since the set \(S\) does not contain an item, the matrix \(H\) is also empty.

  3. Each \(p_{vw}\) is computed by dividing the sum of all weights of incoming ties to \(w\).

  4. Please note that several other probability distributions do not satisfy this property, by having an unbounded support.

  5. Notice that in Eq. 4, the truncation of weight \(W_{vw}\) into the interval \([0,1]\) results in an activation probability \(p'_{vw}\). The reasoning behind this truncation is as follows: in IC, the attempt of a user \(v\) to activate a neighbor user \(w\) is implemented by generating a random number \(r\) that follows uniform distribution in the interval \([0,1]\). The value of \(r\) is then compared to the weight \(W_{vw}\). User \(w\) becomes activated, if \(r < W_{vw}\). Thus, negative values of the weight \(W_{vw}\) correspond to an activation probability \(p'_{vw} = 0\), since in this case it always holds that \(r \nless W_{vw}\). Similarly, values of the weight \(W_{vw}\) that are higher than 1, correspond to an activation probability \(p'_{vw} = 1\), since in this case it always holds that \(r < W_{vw}\).

  6. http://www.public.asu.edu/~jtang20/datasetcode/truststudy.htm.

  7. The small difference in the \(\alpha \) values used with IC and LT (2 and 1.78, respectively) is justified by the results in Fig. 1a, b, which refer to the performance of the SenderRank baseline and, thus, the small discrepancies in the number of activations are due to the subtle differences between the two diffusion models themselves. We selected the \(\alpha \) value for LT accordingly (based on linear interpolation) so that we can more clearly identify in the sequel the performance gains due to link injection, after having first aligned the performance of the SenderRank baseline w.r.t. the two diffusion models.

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Authors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloníki, Greece

    Stefanos Antaris & Dimitrios Rafailidis

  2. Katholische Universität Eichstätt-Ingolstadt, Eichstätt, Germany

    Alexandros Nanopoulos

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  1. Stefanos Antaris
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  2. Dimitrios Rafailidis
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  3. Alexandros Nanopoulos
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Correspondence to Stefanos Antaris.

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Antaris, S., Rafailidis, D. & Nanopoulos, A. Link injection for boosting information spread in social networks. Soc. Netw. Anal. Min. 4, 236 (2014). https://doi.org/10.1007/s13278-014-0236-y

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