Hierarchical, Parameter-Free Community Discovery
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Community detection algorithms: a comparative analysis: invited presentation, extended abstract
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining mobility user profiles for car pooling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A classification for community discovery methods in complex networks
Statistical Analysis and Data Mining
When will it happen?: relationship prediction in heterogeneous information networks
Proceedings of the fifth ACM international conference on Web search and data mining
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The availability of massive network and mobility data from diverse domains has fostered the analysis of human behaviors and interactions. This data availability leads to challenges in the knowledge discovery community. Several different analyses have been performed on the traces of human trajectories, such as understanding the real borders of human mobility or mining social interactions derived from mobility and vice versa. However, the data quality of the digital traces of human mobility has a dramatic impact over the knowledge that it is possible to mine, and this issue has not been thoroughly tackled so far in literature. In this paper, we mine and analyze with complex network techniques a large dataset of human trajectories, a GPS dataset from more than 150k vehicles in Italy. We build a multi resolution grid and we map the trajectories with several complex networks, by connecting the different areas of our region of interest. Then we analyze the structural properties of these networks and the quality of the borders it is possible to infer from them. The result is a significant advancement in our understanding of the data transformation process that is needed to connect mobility with social network analysis and mining.