The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Pfp: parallel fp-growth for query recommendation
Proceedings of the 2008 ACM conference on Recommender systems
The impact of network structure on breaking ties in online social networks: unfollowing on twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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In recent years the importance of user interactions has been recognized in a variety of research contexts. There is a variety of algorithms for modeling these in social graphs; in particular, we distinguish static and dynamic relations. In contrast to static graphs in which the networks do not change over time, the underlying relation is changing frequently in various contexts. This should be reflected by a time dependent social neighborhood of users. In this paper, we present a new and intuitive visualization concept for the histories of user interactions. We derive association rules and visualize these using heatmaps. We demonstrate the impact of the presented approach by several examples utilizing real-world data -- using the well known twitter dump of 2009.