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Cheap, Easy, and Massively Effective Viral Marketing in Social Networks: Truth or Fiction?

机译:在社交网络中便宜,简单,且大规模有效的病毒营销:真理或小说?

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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 increases 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 to 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 O(l) from the optimal solutions in power-law networks and outperforms the greedy heuristics which realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(logn) is unlikely possible.
机译:在线社交网络(OSNS)已成为最有效的营销和广告渠道之一。由于用户经常受到他们朋友的影响,因此“口中”交换所谓的社交网络中的病毒营销可以用于增加产品采用或广泛传播的内容。对病毒营销的常见感知对便宜,简单,大规模效率使其成为传统广告的理想替代。然而,最近的研究表明,传播通常在距离来源的少量啤酒花中迅速消失,抵消了对文学中考虑的影响的自我延续的假设。只有有限的影响繁殖,通过病毒营销大规模地达到客户仍然价格合理?如何在经济上花费更多的资源来增加传播速度?我们调查了经济高效的大型病毒营销问题,考虑到有限的影响传播。基于幂律网络理论的分析分析和数值分析表明,病毒营销可能涉及昂贵的播种。为了最大限度地减少种子成本,我们提供数学规划,以找到中型网络的最佳种子,并提出维拉达,一种有效的算法,以解决大规模网络上的问题。 Virads保证了o(l)的相对误差,从幂律网络中的最佳解决方案,优于贪婪启发式,从而实现了学位中心。此外,我们还表明,通常,近似于比O(LOGN)更好地的最佳种子不太可能。

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