Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

Üçer, Serkan; Özyer, Tansel; Alhajj, Reda

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

dc.authorid	0000-0001-6657-9738
dc.contributor.author	Üçer, Serkan
dc.contributor.author	Özyer, Tansel
dc.contributor.author	Alhajj, Reda
dc.date.accessioned	2022-09-22T06:16:24Z
dc.date.available	2022-09-22T06:16:24Z
dc.date.issued	2022
dc.department	İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which efficiently works with time-series numerical datasets. In this study, we have extended SNAc to work with any type of tabular data by showing its classification efficiency on a broader collection of datasets that may contain numerical and categorical features. This version of GSNAc simply works by transforming traditional tabular data into a network where samples of the tabular dataset are represented as nodes and similarities between the samples are reflected as edges connecting the corresponding nodes. The raw network graph is further simplified and enriched by its edge space to extract a visualizable 'graph classifier model-GCM'. The concept of the GSNAc classification model relies on the study of node similarities over network graphs. In the prediction step, the GSNAc model maps test nodes into GCM, and evaluates their average similarity to classes by employing vectorial and topological metrics. The novel side of this research lies in transforming multidimensional data into a 2D visualizable domain. This is realized by converting a conventional dataset into a network of 'samples' and predicting classes after a careful and detailed network analysis. We exhibit the classification performance of GSNAc as an effective classifier by comparing it with several well-established machine learning classifiers using some popular benchmark datasets. GSNAc has demonstrated superior or comparable performance compared to other classifiers. Additionally, it introduces a visually comprehensible process for the benefit of end-users. As a result, the spin-off contribution of GSNAc lies in the interpretability of the prediction task since the process is human-comprehensible; and it is highly visual.
dc.identifier.citation	Üçer, S., Özyer, T. ve Alhajj, R. (2022). Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier. Scientific Reports, 12(1). http://doi.org/10.1038/s41598-022-19419-7
dc.identifier.doi	10.1038/s41598-022-19419-7
dc.identifier.issn	2045-2322
dc.identifier.issue	1
dc.identifier.pmid	36075941
dc.identifier.scopus	2-s2.0-85137586225
dc.identifier.scopusquality	Q1
dc.identifier.uri	http://doi.org/10.1038/s41598-022-19419-7
dc.identifier.uri	https://hdl.handle.net/20.500.12511/9732
dc.identifier.volume	12
dc.identifier.wos	000852396300002	en_US
dc.identifier.wosquality	Q2
dc.indekslendigikaynak	Web of Science
dc.indekslendigikaynak	Scopus
dc.indekslendigikaynak	PubMed
dc.institutionauthor	Alhajj, Reda
dc.language.iso	en
dc.publisher	Nature Portfolio
dc.relation.ispartof	Scientific Reports	en_US
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rights	Attribution 4.0 International	*
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject	Artificial Intelligence
dc.subject	Social Network
dc.subject	Analysis
dc.title	Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier
dc.type	Article