Event detection via tracking the change in community structure, communication trends, and graph embeddings

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PhD_Thesis_Riza_Aktunc_November_Formatted.pdf

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2022-11-4

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Aktunç, Rıza

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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. 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 are 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. The proposed graph embedding based methods have the potential to produce higher accuracy values in small communication data sets with generally low execution times.

Subject Keywords

Event detection, Community detection, Temporal network, Network features, Change tracking, Community structure, Communication trend, Graph embedding, Ensemble model

URI

https://hdl.handle.net/11511/101124

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

R. Aktunç, “Event detection via tracking the change in community structure, communication trends, and graph embeddings,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.