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Cost-Effective Viral Marketing for Time-Critical Campaigns in Large-Scale Social Networks

机译:大型社交网络中时间紧迫型运动的经济有效病毒式营销

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Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, “word-of-mouth” exchanges, so-called viral marketing, in social networks can be used to increase product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How do we economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of from the optimal solutions in power-law networks and outperforms the greedy heuristics that realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than is unlikely possible.
机译:在线社交网络(OSN)已成为最有效的营销和广告渠道之一。由于用户经常受到朋友的影响,因此可以使用社交网络中的“口碑”交流(所谓的病毒式营销)来增加产品采用率或在网络上广泛传播内容。对病毒式营销的普遍认识是廉价,简便和高效,这使其成为传统广告的理想替代品。但是,最近的研究表明,传播通常仅在源头的几跳之内迅速消失,这与文献中考虑的影响力的自我延续的假设相抵触。在影响力传播有限的情况下,通过病毒式营销大规模吸引客户是否仍然可以承受?我们如何在经济上花费更多的资源来提高传播速度?考虑到有限的影响传播,我们调查了具有成本效益的大规模病毒营销问题。基于幂律网络理论的分析分析和数值分析都表明,病毒式营销可能涉及昂贵的播种。为了最大程度地降低播种成本,我们提供了数学编程,以为中型网络找到最佳播种,并提出了一种有效的算法VirAds来解决大规模网络上的问题。 VirAds保证了幂律网络中最优解决方案的相对误差范围,并且胜过了在程度中心性上实现的贪婪启发式算法。此外,我们还表明,一般而言,以比不可能的更好的比率近似最佳播种。

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