Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Relations between average case complexity and approximation complexity
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-unit auctions with budget-constrained bidders
Proceedings of the 6th ACM conference on Electronic commerce
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Budget optimization in search-based advertising auctions
Proceedings of the 8th ACM conference on Electronic commerce
Ruling Out PTAS for Graph Min-Bisection, Dense k-Subgraph, and Bipartite Clique
SIAM Journal on Computing
Multi-unit Auctions with Budget Limits
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Detecting high log-densities: an O(n¼) approximation for densest k-subgraph
Proceedings of the forty-second ACM symposium on Theory of computing
Budget constrained auctions with heterogeneous items
Proceedings of the forty-second ACM symposium on Theory of computing
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
On the approximability of budget feasible mechanisms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
A note on maximizing a submodular set function subject to a knapsack constraint
Operations Research Letters
Proceedings of the 23rd international conference on World wide web
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Brands and agencies use marketing as a tool to influence customers. One of the major decisions in a marketing plan deals with the allocation of a given budget among media channels in order to maximize the impact on a set of potential customers. A similar situation occurs in a social network, where a marketing budget needs to be distributed among a set of potential influencers in a way that provides high-impact. We introduce several probabilistic models to capture the above scenarios. The common setting of these models consists of a bipartite graph of source and target nodes. The objective is to allocate a fixed budget among the source nodes to maximize the expected number of influenced target nodes. The concrete way in which source nodes influence target nodes depends on the underlying model. We primarily consider two models: a source-side influence model, in which a source node that is allocated a budget of k makes k independent trials to influence each of its neighboring target nodes, and a target-side influence model, in which a target node becomes influenced according to a specified rule that depends on the overall budget allocated to its neighbors. Our main results are an optimal (1-1/e)-approximation algorithm for the source-side model, and several inapproximability results for the target-side model, establishing that influence maximization in the latter model is provably harder.