Randomized algorithms
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Proceedings of the fifth ACM international conference on Web search and data mining
Controlling opinion bias in online social networks
Proceedings of the 3rd Annual ACM Web Science Conference
Influence spread in large-scale social networks --- a belief propagation approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
I act, therefore I judge: network sentiment dynamics based on user activity change
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Controlling opinion propagation in online networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
| Hi-index | 0.89 |
We consider the spread maximization problem that was defined by Domingos and Richardson (2001, 2002) [7,22]. In this problem, we are given a social network represented as a graph and are required to find the set of the most ''influential'' individuals that by introducing them with a new technology, we maximize the expected number of individuals in the network, later in time, that adopt the new technology. This problem has applications in viral marketing, where a company may wish to spread the rumor of a new product via the most influential individuals in popular social networks such as Myspace and Blogsphere. The spread maximization problem was recently studied in several models of social networks (Kempe et al. (2003, 2005) [14,15], Mossel and Roch (2007) [20]). In this short paper we study this problem in the context of the well studied probabilistic voter model. We provide very simple and efficient algorithms for solving this problem. An interesting special case of our result is that the most natural heuristic solution, which picks the nodes in the network with the highest degree, is indeed the optimal solution.