Abstract
Information diffusion is a fundamental process taking place in networks. It is often possible to observe when nodes get influenced, but it is hard to directly observe the underlying network. Furthermore, in many applications, the underlying networks are implicit or even unknown. Existing works on network inference can only infer influential edges between two nodes. In this paper, we develop a method for inferring top-k maximal influential paths which can capture the dynamics of information diffusion better compared to influential edges. We define a generative influence propagation model based on the Independent Cascade Model and Linear Threshold Model, which mathematically model the spread of certain information through a network. We formalize the top-k maximal influential path inference problem and develop an efficient algorithm, called TIP, to infer the top-k maximal influential paths. TIP makes use of the properties of top-k maximal influential paths to dynamically increase the support and prune the projected databases. We evaluate the proposed algorithms on both synthetic and real world data sets. The experimental results demonstrate the effectiveness and efficiency of our method.
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Xu, E., Hsu, W., Lee, M.L., Patel, D. (2012). Top-k Maximal Influential Paths in Network Data. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32600-4_27
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DOI: https://doi.org/10.1007/978-3-642-32600-4_27
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