# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 5 Bibliometric Overview of Retrieved Publications  
This section provides a general overview of the retrieved records, specifically analyzing 136 records obtained after removing duplicates. This broader inclusion criterion was chosen because the available material is somewhat limited, and excluding results based on previously established inclusion/exclusion criteria would not significantly impact the current analysis. The analysis focuses primarily on bibliographic information. The diagram shown in Figure 15 highlights how interest in the explainability and interpretability of drift has increased over time, with a notable growth of approximately 80% from 2020 to 2024. This upward trend suggests a continued expansion of the topic in the upcoming years.  
Figure 16 presents the distribution of corresponding authors by country, providing insights into the global scope of research activities on this topic. The majority of research originates from China, Germany, and the USA, countries known for rapidly expanding industries. This distribution underscores the international relevance of the topic, especially within leading industrial nations, and emphasizes that explainability and interpretability  
\begin{table}
\begin{tabular}{c c} \hline
**Quartile** & **Paper** \\ \hline \(Q1\) & [22; 24; 25; 26; 28; 29; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125] \\ \(Q2\) & [126; 127  
52 different journals. Among these, only four journals contain more than one publication within the collected records. This fragmented distribution, predominantly within computer science journals, likely results from the novelty of the research area, the absence of specialized journals explicitly dedicated to these topics, and the broad relevance of drift explainability across various computer science subfields, including machine learning, data mining, and artificial intelligence. As the field matures, we anticipate greater consolidation around a smaller number of key journals.  
Similarly, Table A2 lists all conferences and workshops where the bibliography records were published. Mirroring the journal distribution, we identified 52 distinct conferences or workshops featuring papers addressing the explainability and interpretability of concept and data drift. Among these, 10 conferences stand out for hosting multiple publications within the collected records. Notably, three conferences--BPM, CIKM, and ECML PKDD--each featured three publications, followed by seven additional conferences with two publications each. The overwhelming majority of these conferences focus on computer science topics with broad applicability, with the International Petroleum Conference being the sole exception. Furthermore, the table highlights the global scope of these conferences, spanning Asia, Australia, Europe, and international venues, reinforcing the homogeneous geographic distribution of research on this topic, as previously illustrated in the pie chart in Figure 16.  
To assess the extent of researcher collaboration, a co-authorship network was created using VOSviewer software [148]. Figure 17a visualizes authors as nodes and their collaborations as connections, revealing distinct research clusters. Node colors represent authors' average publication years, enabling an analysis of how research groups have evolved over time. Two key insights emerge from this visualization: first, the numerous clusters point to significant fragmentation within the research community; second, the prevalence of nodes colored yellow suggests that many research groups have recently formed and thus exhibit relatively low average publication frequencies, a finding consistent with the observed upward trend in interest. Nevertheless, there are also clusters showing considerable collaboration, primarily associated with somewhat older publications. Two prominent clusters are expanded and detailed further in Figure 17b.  
Figure 17: _Cont._