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Mining knowledge-sharing sites for viral marketing
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Maximizing the spread of influence through a social network
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Information diffusion through blogspace
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Cost-effective outbreak detection in networks
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Efficient influence maximization in social networks
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Social influence analysis in large-scale networks
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Tractable models for information diffusion in social networks
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Mining topic-level influence in heterogeneous networks
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CELF++: optimizing the greedy algorithm for influence maximization in social networks
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Assessing and ranking structural correlations in graphs
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Sparsification of influence networks
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LikeMiner: a system for mining the power of 'like' in social media networks
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Active learning of model parameters for influence maximization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
A data-based approach to social influence maximization
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What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Proceedings of the fifth ACM international conference on Web search and data mining
In-time estimation for influence maximization in large-scale social networks
Proceedings of the Fifth Workshop on Social Network Systems
Recommendations to boost content spread in social networks
Proceedings of the 21st international conference on World Wide Web
Finding influential seed successors in social networks
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Influence propagation and maximization for heterogeneous social networks
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SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Identifying influential agents for advertising in multi-agent markets
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Exploring social influence for recommendation: a generative model approach
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Making recommendations in a microblog to improve the impact of a focal user
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Maximizing influence spread in a new propagation model
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Containment of misinformation spread in online social networks
Proceedings of the 3rd Annual ACM Web Science Conference
Probabilistic macro behavioral targeting
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The walls have ears: optimize sharing for visibility and privacy in online social networks
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On bundle configuration for viral marketing in social networks
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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
On approximation of real-world influence spread
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CINEMA: conformity-aware greedy algorithm for influence maximization in online social networks
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Which targets to contact first to maximize influence over social network
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Large Social Networks Can Be Targeted for Viral Marketing with Small Seed Sets
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Meme ranking to maximize posts virality in microblogging platforms
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Discovering influential nodes from trust network
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Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
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Detecting changes in information diffusion patterns over social networks
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Social capital: the power of influencers in networks
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Fast greedy algorithms in mapreduce and streaming
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STRIP: stream learning of influence probabilities
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Finding influencers in networks using social capital
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Hierarchical influence maximization for advertising in multi-agent markets
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Analysis of misinformation containment in online social networks
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StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
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Personalized influence maximization on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Content-centric flow mining for influence analysis in social streams
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Probabilistic solutions of influence propagation on social networks
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Guide query in social networks
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Exploring celebrity dynamics on Twitter
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A probability based algorithm for influence maximization in social networks
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Pagerank with priors: an influence propagation perspective
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Prediction in a microblog hybrid network using bonacich potential
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How to influence people with partial incentives
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Weighted graph-based methods for identifying the most influential actors in trust social networks
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IBM Journal of Research and Development
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Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread --- it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100%--260% increase in influence spread.