@inproceedings{fdde1f3345c94a9390e2c697fca93beb,
title = "Community verification with topic modeling",
abstract = "Different performance measurement metrics have been proposed to evaluate the performance of community detection algorithms, such as modularity, conductance, etc. However, there is few work which makes sense of a community, that is, explain what does the community do, what is the community{\textquoteright}s interest. In this paper, we use topic modeling to capture the topics of users in the same community and verify a heuristic community detection algorithm by showing that the users in the communities share strong interests.",
keywords = "Community detection, LDA, Social media, Topic modeling",
author = "Feng Wang and Ken Orton",
note = "Funding Information: This project is supported by NSF grant CNS #1218212. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 12th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2017 ; Conference date: 19-06-2017 Through 21-06-2017",
year = "2017",
doi = "10.1007/978-3-319-60033-8_25",
language = "English (US)",
isbn = "9783319600321",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "278--288",
editor = "Yan Zhang and Abdallah Khreishah and Mingyuan Yan and Liran Ma",
booktitle = "Wireless Algorithms, Systems, and Applications - 12th International Conference, WASA 2017, Proceedings",
}