Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks

2020-01-01
Aktunç, Rıza
Toroslu, İsmail Hakkı
Karagöz, Pınar
Show full item record
270
views
0
downloads
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 developed using different models. Furthermore, ensemble methods combining the change models are proposed and their event detection performances are analyzed, as well. Experiments conducted on benchmark data set show that community change can be used as an indicator of event, and ensemble model further improves the event detection performance.
Event detection, Temporal network, Community detection, Network features, Change tracking, Ensemble model
https://link.springer.com/chapter/10.1007/978-3-030-33698-1_5
https://hdl.handle.net/11511/83370
Department of Computer Engineering, Book / Book chapter

Suggestions

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 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 by Change Tracking on Community Structure of Temporal Networks
Aktunc, Riza; Toroslu, İsmail Hakkı; Karagöz, Pınar (2018-08-31)
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. Experime...
Event detection on social media using transaction based stream processing engine
Çınar, Hüseyin Alper; Karagöz, Pınar; Department of Computer Engineering (2019)
The aim of this study is detecting events on social media by improving current solutions in terms of accuracy and time performance. An event is something that occurs in a short duration of time in a certain place. In this thesis, the problem is modelled as a streaming transaction process. Three different event detection method is adapted to our solution. First one is the keyword-based event detection method that looks for bursty keywords in a period. The second one is the clustering-based event detection me...
Interpretable spatio-temporal networks for modeling and forecasting societal events
Ertuğrul, Ali Mert; Taşkaya Temizel, Tuğba; Department of Information Systems (2019)
The relationships between individual activities and societal events (e.g. migrations, social movements) are complex due to the various social, temporal and spatial factors. Understanding such relationships in the context of various societal events such as street protests and opioid crisis, and forecasting these events are important as they have great impacts on public policies and supporting decision making of authorities. In this thesis, novel, spatio-temporal, deep neural networks are proposed (i) to fore...
Citation Formats
R. Aktunç, İ. H. Toroslu, and P. Karagöz, Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks. 2020, p. 96.
METU IIS - Integrated Information Service open@metu.edu.tr