Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering leaders from community actions
Proceedings of the 17th ACM conference on Information and knowledge management
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Where the blogs tip: connectors, mavens, salesmen and translators of the blogosphere
Proceedings of the First Workshop on Social Media Analytics
Structural trend analysis for online social networks
Proceedings of the VLDB Endowment
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Information diffusion in social networks provide great opportunities for political and social change as well as societal education. Therefore understanding information diffusion in social networks is a critical research goal. This greater understanding can be achieved through data analysis, development of reliable models that can predict outcomes of social processes, and ultimately the creation of applications that can shape the outcome of these processes. In this tutorial, we aim to provide an overview of such recent research based on a wide variety of techniques such as optimization algorithms, data mining, data streams covering a large number of problems such as influence spread maximization, misinformation limitation and study of trends in online social networks.