Event Detection by Change Tracking on Community Structure of Temporal Networks

Date

2018-08-31

Author

Aktunc, Riza
Toroslu, İsmail Hakkı
Karagöz, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

182
views

0
downloads

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.

Subject Keywords

Event detection, Temporal network, Community detection, Network features, Change

URI

https://hdl.handle.net/11511/52701

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Event detection via tracking the change in community structure, communication trends, and graph embeddings Aktunç, Rıza; Karagöz, Pınar; Toroslu, İsmail Hakkı; Department of Computer Engineering (2022-11-4) Event detection is a popular research problem aiming to detect events from various data sources, such as climate records, traffic data, news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure, communication trends, and graph embeddings. With this aim, various community structure, communication trend, and graph embedding based event detection methods are proposed. Additi...
Event Detection via Tracking the Change in Community Structure and Communication Trends Aktunc, Riza; Karagöz, Pınar; Toroslu, Ismail Hakki (2022-01-01) Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change trac...
Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks Aktunç, Rıza; Toroslu, İsmail Hakkı; Karagöz, Pınar (Springer, Cham, 2020-01-01) In this work, we focus on social interactions in communities in order to detect events. There are several previous efforts for the event detection problem based on analyzing the change in the network structure in terms of the overall network features. However, in this work, event detection is considered as a problem of change detection in community structures. Particularly, communities extracted from communication network are focused on, and various versions of the community change detection methods are dev...
Streaming Event Detection in Microblogs: Balancing Accuracy and Performance SAHIN, OZLEM CEREN; Karagöz, Pınar; TATBUL, NESIME (2019-06-14) In this work, we model the problem of online event detection in microblogs as a stateful stream processing problem and offer a novel solution that balances result accuracy and performance. Our new approach builds on two state of the art algorithms. The first algorithm is based on identifying bursty keywords inside blocks of blog messages. The second one involves clustering blog messages based on similarity of their contents. To combine the computational simplicity of the keyword-based algorithm with the sem...
Word Embedding Based Event Detection on Social Media Ertugrul, Ali Mert; Velioglu, Burak; Karagöz, Pınar (2017-06-23) Event detection from social media messages is conventionally based on clustering the message contents. The most basic approach is representing messages in terms of term vectors that are constructed through traditional natural language processing (NLP) methods and then assigning weights to terms generally based on frequency. In this study, we use neural feature extraction approach and explore the performance of event detection under the use of word embeddings. Using a corpus of a set of tweets, message terms...

Citation Formats

R. Aktunc, İ. H. Toroslu, and P. Karagöz, “Event Detection by Change Tracking on Community Structure of Temporal Networks,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52701.