Detecting Communities on Topic of Transportation With Sparse Crowd Annotations

Social networks contain a large amount of information on transportation, e.g., traffic accidents, congestions, and vehicles. Such information is the original ideas of people with respect to real-world transportation issues, and detecting communities on the topic of transportation from the information will benefit many ITS applications. However, realworld social network nodes often contain multiple attributes, and the network can be very large. The two properties can lead to confusion and unscalability problem to clustering methods. In this paper, we propose a semisupervised method, namely, Transportation Community Detection (TRACED), to address this problem. TRACED allows multiple individuals to select their familiar nodes as the participants of a certain community, and thus, the confusion of multiple attributes can be largely reduced. Moreover, the proposed method can be expanded to large networks since it is able to conduct an effective clustering with low time complexity. With the help of TRACED, we can detect densely connected communities on the topic of transportation for further studies.