On the supermodular knapsack problem
Mathematical Programming: Series A and B
Generating random combinatorial objects
Journal of Algorithms
The random walk construction of uniform spanning trees and uniform labelled trees
SIAM Journal on Discrete Mathematics
Generating random spanning trees more quickly than the cover time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
The Mathematics of Infectious Diseases
SIAM Review
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
On computer viral infection and the effect of immunization
ACSAC '00 Proceedings of the 16th Annual Computer Security Applications Conference
Measuring and Modeling Computer Virus Prevalence
SP '93 Proceedings of the 1993 IEEE Symposium on Security and Privacy
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
Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
On the windfall of friendship: inoculation strategies on social networks
Proceedings of the 9th ACM conference on Electronic commerce
Generating random spanning trees
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Word of Mouth: Rumor Dissemination in Social Networks
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
Submodular Approximation: Sampling-based Algorithms and Lower Bounds
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Inferring networks of diffusion and influence
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
Competing for customers in a social network: the quasi-linear case
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
Structural trend analysis for online social networks
Proceedings of the VLDB Endowment
Data-driven modeling and analysis of online social networks
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Information diffusion in social networks: observing and affecting what society cares about
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal incentive timing strategies for product marketing on social networks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Competing memes propagation on networks: a case study of composite networks
ACM SIGCOMM Computer Communication Review
Containment of misinformation spread in online social networks
Proceedings of the 3rd Annual ACM Web Science Conference
Making your interests follow you on twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the sixth ACM international conference on Web search and data mining
Security games with contagion: handling asymmetric information
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
The bang for the buck: fair competitive viral marketing from the host perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of misinformation containment in online social networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Epidemiological modeling of news and rumors on Twitter
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Spatio-temporal and events based analysis of topic popularity in twitter
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Personalized influence maximization on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Two decades of internet video streaming: A retrospective view
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
A new approach to identify influential spreaders in complex networks
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Finding critical blocks of information diffusion in social networks
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Probabilistic graph summarization
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A probability based algorithm for influence maximization in social networks
Proceedings of the 5th Asia-Pacific Symposium on Internetware
Proceedings of the 19th international conference on Intelligent User Interfaces
A cutting-plane algorithm for solving a weighted influence interdiction problem
Computational Optimization and Applications
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In this work, we study the notion of competing campaigns in a social network and address the problem of influence limitation where a "bad" campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or "good") campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. We show that this optimization problem is NP-hard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. We experimentally compare the performance of the greedy method to various heuristics. The experiments reveal that in most cases inexpensive heuristics such as degree centrality compare well with the greedy approach. We also study the influence limitation problem in the presence of missing data where the current states of nodes in the network are only known with a certain probability and show that prediction in this setting is a supermodular problem. We propose a prediction algorithm that is based on generating random spanning trees and evaluate the performance of this approach. The experiments reveal that using the prediction algorithm, we are able to tolerate about 90% missing data before the performance of the algorithm starts degrading and even with large amounts of missing data the performance degrades only to 75% of the performance that would be achieved with complete data.