Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Efficient identification of starters and followers in social media
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Measurement-calibrated graph models for social network experiments
Proceedings of the 19th international conference on World wide web
AMT'10 Proceedings of the 6th international conference on Active media technology
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Seeder finder: identifying additional needles in the Twitter haystack
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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Much research effort has been conducted to analyze information from social networks, including finding the influential users. In this paper, we propose a graph model to represent the relationships between online posts of one topic, in order to identify the influential users. Besides the role of starters, we suggest a new role, the connecter, to help bridging two different clusters of posts. Three methods for measuring the influences of online posts are discussed to distinguish starters and connecters in the graph. The results of the different measurements can then be integrated to determine the most influential posts and their respective authors. With the information of the explicit and implicit relationship between posts, our model tries to identify the most influential users based on their direct interaction as well as the implicit relationship among postings. The experiment is performed on Twitter to verify the model and the three methods of influence measurement. The interpretation of the methods is also given to justify the experiment results.