Paper Info
Title
Event Detection by Change Tracking on Community Structure of Temporal Networks.
Abstract
Event detection is a popular research problem, aiming to detect events from online data sources with least possible delay. Most of the previous work focus on analyzing textual content such as social media postings to detect happenings. In this work, we consider event detection as a change detection problem in network structure, and propose a method that detects change in community structure extracted from communication network. We study three versions of the method based on different change models. Experimental analysis on benchmark data set reveals that change in the community can be used as an indication of an event.
Year
DOI
Venue
2018
10.5555/3382225.3382422
ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining Barcelona Spain August, 2018
Keywords
Field
DocType
Event detection, temporal network, community detection, network features, change
Data mining,Community structure,Change detection,Social media,Telecommunications network,Computer science,Change tracking,Artificial intelligence,Machine learning,Network structure
Conference
ISBN
Citations 
PageRank 
978-1-5386-6051-5
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Riza Aktunc1141.37
Ismail Hakki Toroslu2456102.80
Pinar Karagoz315428.34