HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
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
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
A data-based approach to social influence maximization
Proceedings of the VLDB Endowment
CASINO: towards conformity-aware social influence analysis in online social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Influence maximization (IM) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by state-of-the-art greedy IM techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large real-world networks. Secondly, although extensive research in social psychology suggests that humans will readily conform to the wishes or beliefs of others, surprisingly, existing IM techniques are conformity-unaware. That is, they only utilize an individual's ability to influence another but ignores conformity (a person's inclination to be influenced) of the individuals. In this paper, we propose a novel conformity-aware cascade (c2) model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying data for estimating influence spreads. We propose a novel greedy algorithm called CINEMA that generates high quality seed set by exploiting this model. It first partitions the network into a set of non-overlapping subnetworks and for each of these subnetworks it computes the influence and conformity indices of nodes. Each subnetwork is then associated with a COG-sublist which stores the marginal gains of the nodes in the subnetwork in descending order. The node with maximum marginal gain in each COG-sublist is stored in a data structure called MAG-list. These structures are manipulated by CINEMA to efficiently find the seed set. A key feature of such partitioning-based strategy is that each node's influence computation and updates can be limited to the subnetwork it resides instead of the entire network. Our empirical study with real-world social networks demonstrates that CINEMA generates superior quality seed set compared to state-of-the-art IM approaches.