Approximation algorithms for directed Steiner problems
Journal of Algorithms
Mining the network value of customers
Proceedings of the seventh 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
Identifying early buyers from purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
ACM SIGKDD Explorations Newsletter
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Hierarchical topic segmentation of websites
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Inoculation strategies for victims of viruses and the sum-of-squares partition problem
Journal of Computer and System Sciences
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Epidemic thresholds in real networks
ACM Transactions on Information and System Security (TISSEC)
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Efficient identification of starters and followers in social media
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
An O(pn2) algorithm for the p -median and related problems on tree graphs
Operations Research Letters
Finding influential mediators in social networks
Proceedings of the 20th international conference companion on World wide web
Discovering shakers from evolving entities via cascading graph inference
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding influential seed successors in social networks
Proceedings of the 21st international conference companion on World Wide Web
Influence propagation and maximization for heterogeneous social networks
Proceedings of the 21st international conference companion on World Wide Web
Dynamic selection of activation targets to boost the influence spread in social networks
Proceedings of the 21st international conference companion on World Wide Web
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Interacting viruses in networks: can both survive?
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding and managing cascades on large graphs
Proceedings of the VLDB Endowment
Gelling, and melting, large graphs by edge manipulation
Proceedings of the 21st ACM international conference on Information and knowledge management
Feature-Enhanced probabilistic models for diffusion network inference
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Extracting social events for learning better information diffusion models
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Recovering information recipients in social media via provenance
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Seeking provenance of information using social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A tool for assisting provenance search in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Assume a network (V,E) where a subset of the nodes in V are active. We consider the problem of selecting a set of k active nodes that best explain the observed activation state, under a given information-propagation model. We call these nodes effectors. We formally define the k-Effectors problem and study its complexity for different types of graphs. We show that for arbitrary graphs the problem is not only NP-hard to solve optimally, but also NP-hard to approximate. We also show that, for some special cases, the problem can be solved optimally in polynomial time using a dynamic-programming algorithm. To the best of our knowledge, this is the first work to consider the k-Effectors problem in networks. We experimentally evaluate our algorithms using the DBLP co-authorship graph, where we search for effectors of topics that appear in research papers.