A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Non-approximability results for optimization problems on bounded degree instances
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Random evolution in massive graphs
Handbook of massive data sets
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Computational Complexity: A Modern Approach
Computational Complexity: A Modern Approach
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Positive Influence Dominating Set in Online Social Networks
COCOA '09 Proceedings of the 3rd International Conference on Combinatorial Optimization and Applications
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Latency-Bounded Minimum Influential Node Selection in Social Networks
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Power-Law Distributions in Empirical Data
SIAM Review
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
On the Approximability of Influence in Social Networks
SIAM Journal on Discrete Mathematics
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Containment of misinformation spread in online social networks
Proceedings of the 3rd Annual ACM Web Science Conference
Analysis of misinformation containment in online social networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A cutting-plane algorithm for solving a weighted influence interdiction problem
Computational Optimization and Applications
On the approximability of positive influence dominating set in social networks
Journal of Combinatorial Optimization
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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(1) 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(log n) is unlikely possible.