Approximation algorithms for directed Steiner problems
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on 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
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Lineage retrieval for scientific data processing: a survey
ACM Computing Surveys (CSUR)
A survey of data provenance in e-science
ACM SIGMOD Record
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Mining Taverna's semantic web of provenance
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Finding effectors in social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Rumors in a Network: Who's the Culprit?
IEEE Transactions on Information Theory
Spotting Culprits in Epidemics: How Many and Which Ones?
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
A tool for collecting provenance data in social media
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Provenance Data in Social Media
Provenance Data in Social Media
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In recent years, social media has changed the way we interact and communicate. Although the existing structure of social media allows users to easily create, receive, and propagate pieces of information, many a time, users do not have background knowledge about the received information, including the provenance (sources or originators) of information, and other recipients who may have retransmitted or modified the information. Providing such additional context to the received information can help users know how much value, trust, and validity should be placed in received information. To judge the credibility of the received piece of information, it is vital to know who are its sources, and how information propagates from sources to other social media users. In this paper, we are studying a novel research problem that facilitates a few known recipients to recover other unknown recipients, and seek the provenance of information. The experimental results with Facebook and Twitter datasets show that the proposed algorithm is effective in correctly recovering the unknown recipients and seeking the provenance of information.