تجزیه و تحلیل رقابتی مشکلات بیشینه سازی نفوذ با اطلاعات ناقص: یک چارچوب الگوریتم تقریبی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|42570||2015||18 صفحه PDF||سفارش دهید||15160 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Performance Evaluation, Volume 91, September 2015, Pages 187–204
Given the popularity of the viral marketing campaign in online social networks, finding a computationally efficient method to identify a set of most influential nodes so as to compete well with others is of the utmost importance. In this paper, we propose a general model to describe the influence propagation of multiple competing sources in the same network. We formulate the Competitive Influence Maximization with Partial information (CIMP) problem: given an influence propagation model and the probability of a node being in the competitor’s seed set, how to find a set of kk seeds so to trigger the largest expected influence cascade under the presence of other competitors? We propose a general algorithmic framework, Two-phase Competitive Influence Maximization (TCIM), to address the CIMP problem. TCIM returns a (1−1/e−ϵ)(1−1/e−ϵ)-approximate solution with probability of at least 1−n−ℓ1−n−ℓ, where ℓ≥1/2ℓ≥1/2 is a parameter controlling the trade-off between the success probability and the computational efficiency. TCIM has an efficient expected time complexity of O(c(k+ℓ)(m+n)logn/ϵ2)O(c(k+ℓ)(m+n)logn/ϵ2), where nn and mm are the number of nodes and edges in the network, and cc is a function of the given propagation model (which may depend on kk and the underlying network). To the best of our knowledge, this is the first work which provides a general framework for the competitive influence maximization problem where the seeds of the competitor could be given as an explicit set of seed nodes or a probability distribution of seed nodes. Moreover, our algorithmic framework provides both quality guarantee of solution and practical computational efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, for the case where the seed set of the competitor is given explicitly, we achieve up to four orders of magnitude speedup as compared to previous algorithms with the same quality guarantee. When the competitor’s seed set is not given explicitly, running TCIM using the probability distribution of the competitor’s seeds returns nodes with higher expected influence than those nodes returned by TCIM using an explicit guess of the competitor’s seeds.