Abstract
As traditional advertising model exposes its weakness of ignoring consumer interests, the concept of narrow advertising draws increasingly more attention which considers the feature of each user. Under this specific environment, effective viral marketing has to select a set of initial users to maximize their influence on the targeted customers. This paper aims at the integration of viral marketing and narrow advertising, by proposing a novel problem called attribute-based influence maximization. Firstly, the problem definition is presented with the consideration of user features. Then the influence probability between two nodes is modeled and two heuristic algorithms, Sum of Probability Covered Algorithm (SoPCA) and Community-based Algorithm (CBA), are designed. Finally, experiments on six datasets are conducted to verify the effectiveness of proposed algorithms.
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Notes
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Targeted nodes in this paper all represent the nodes whose similarity with targeted individual is not equal to 0.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (61272531, 61202449, 61272054, 61370207, 61370208, 61300024, 61320106007 and 61472081), China high technology 863 program (2013AA013503), Jiangsu Technology Planning Program (SBY2014021039-10), Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No. BM2003201 and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No. 93k-9.
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Cao, J. et al. (2016). Attribute-Based Influence Maximization in Social Networks. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_1
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