The nature of statistical learning theory
The nature of statistical learning theory
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Buddy tracking - efficient proximity detection among mobile friends
Pervasive and Mobile Computing
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Behavioral Inference across Cultures: Using Telephones as a Cultural Lens
IEEE Intelligent Systems
VENETA: Serverless Friend-of-Friend Detection in Mobile Social Networking
WIMOB '08 Proceedings of the 2008 IEEE International Conference on Wireless & Mobile Computing, Networking & Communication
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Characterizing individual communication patterns
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Eight friends are enough: social graph approximation via public listings
Proceedings of the Second ACM EuroSys Workshop on Social Network Systems
Four billion little brothers?: privacy, mobile phones, and ubiquitous data collection
Communications of the ACM - Scratch Programming for All
Understanding transportation modes based on GPS data for web applications
ACM Transactions on the Web (TWEB)
Adequacy of Data for Mining Individual Friendship Pattern from Cellular Phone Call Logs
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 05
Exploiting social interactions in mobile systems
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Using Mobile Phones to Nurture Social Networks
IEEE Pervasive Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Scalable Learning of Collective Behavior
IEEE Transactions on Knowledge and Data Engineering
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings
IEEE Transactions on Knowledge and Data Engineering
Dense Subgraph Extraction with Application to Community Detection
IEEE Transactions on Knowledge and Data Engineering
Finding email correspondents in online social networks
World Wide Web
Mining most frequently changing component in evolving graphs
World Wide Web
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Mobile and pervasive computing technologies enable us to obtain real-world sensing data for sociological studies, such as exploring human behaviors and relationships. In this paper, we present a study of understanding social relationship evolution by using real-life anonymized mobile phone data. First, we define a friendship as a directed relation, i.e., person A regards another person B as his or her friend but not necessarily vice versa. Second, we recognize human friendship from a supervised learning perspective. The Support Vector Machine (SVM) approach is adopted as the inference model to predict friendship based on a variety of features extracted from the mobile phone data, including proximity, outgoing calls, outgoing text messages, incoming calls, and incoming text messages. Third, we demonstrate the social relation evolution process by using the social balance theory. For the friendship prediction, we achieved an overall recognition rate of 97.0 % by number and a class average accuracy of 89.8 %. This shows that social relationships (not only reciprocal friends and non-friends, but non-reciprocal friends) can be likely predicted by using real-world sensing data. With respect to the friendship evolution, we verified that the principles of reciprocality and transitivity play an important role in social relation evolution.