Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Linear Time Histogram Metric for Improved SIFT Matching
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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In recent years, social networking sites and social media have become a very important part of peoples' lives, driving everything from family relationships to revolutions. In this work, we study the different patterns of interaction behavior seen in an online social network. We investigate the difference in the relative time people allocate to their friends versus that which their friends allocate to them, and propose a measure for this difference in time allocation. The distribution of this measure is used to identify classes of social agents through agglomerative hierarchical clustering. These classes are then characterized in terms of two important structural attributes: Degree distributions and clustering coefficients. We demonstrate our approach on two large social networks obtained from Facebook. For each network we have the list of all social interactions that took place over six months. The total number of users in the two networks is 939,453 and 841,456, with 1.4 million and 8.4 million interactions, respectively. Our results show that, based the interaction behavior, there are four main classes of agents in both networks, and that they are consistent across the two networks. Furthermore, each class is characterized by a specific profile of degree distributions and clustering coefficients, which are also consistent across both networks. We speculate that agents corresponding to the four classes play different roles in the social network. To test this, we developed an opinion propagation model where opinions are represented as m-bit strings communicated from agent to agents. An agent receiving an opinion then selectively modifies its own opinions depending on the social and informational value it places upon communications from the sending agent, its overall agreement with the sending agent, and its own propensties. Opinions are injected into the system by agents of specific classes and their spread is tracked by propagating tags. The resulting data is used to analyze the influence of agents from each class in the viral spread of ideas under various conditions. The analysis also shows what behavioral factors at the agent level have the most significant impact on the spreading of ideas.