Event Detection via Tracking the Change in Community Structure and Communication Trends

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2022-01-01

Author

Aktunc, Riza
Karagöz, Pınar
Toroslu, Ismail Hakki

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

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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 tracking is presented such that the proposed algorithms can focus on communities with different size ranges, and considerable time efficiency can be obtained. The event detection performance of the proposed methods is analyzed using a set of real world and benchmark data sets in comparison to previous solutions in the literature. The experiments show that the proposed methods have higher event detection accuracy than the baseline methods. Additionally, their scalability is presented through analysis by using high volume of communication data. Among the proposed methods, CN-NEW, which is a community structure based method, performs the best on the overall. The proposed communication trend based methods perform better mostly on communication data sets (such as CDR), whereas community structure based methods tend to perform better on social media-based data sets.

Subject Keywords

Event detection, Market research, Mathematical models, Electronic mail, Social networking (online), Scalability, Measurement, Tracking, Change tracking, communication trends, community detection, community structure, ensemble model, event detection, network features, temporal network

URI

https://hdl.handle.net/11511/101115

Journal

IEEE ACCESS

DOI

https://doi.org/10.1109/access.2022.3214543

Collections

Department of Computer Engineering, Article

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Citation Formats

R. Aktunc, P. Karagöz, and I. H. Toroslu, “Event Detection via Tracking the Change in Community Structure and Communication Trends,” IEEE ACCESS, vol. 10, pp. 109712–109728, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/101115.