Scientific Impact of Graph-Based Approaches in Deep Learning Studies -- A Bibliometric Comparison

Applying graph-based approaches in deep learning receives more attention over time. This study presents statistical analysis on the use of graph-based approaches in deep learning and examines the scientific impact of the related articles. Processing the data obtained from the Web of Science database, metrics such as the type of the articles, funding availability, indexing type, annual average number of citations and the number of access were analyzed to quantitatively reveal the effects on the scientific audience. It's outlined that deep learning-based studies gained momentum after year 2013, and the rate of graph-based approaches in all deep learning studies increased linearly from 1% to 4% within the following 10 years. Conference publications scanned in the Conference Proceeding Citation Index (CPCI) on the graph-based approaches receive significantly more citations. The citation counts of the SCI-Expanded and Emerging SCI indexed publications of the two streams are close to each other. While the citation performances of the supported and unsupported publications of the two sides were similar, pure deep learning studies received more citations on the journal publication side and graph-based approaches received more citations on the conference side. Despite their similar performance in recent years, graph-based studies show twice more citation performance as they get older, compared to traditional approaches. Annual average citation performance per article for all deep learning studies is 11.051 in 2014, while it is 22.483 for graph-based studies. Also, despite receiving 16% more access, graph-based papers get almost the same overall citation over time with the pure counterpart. This is an indication that graph-based approaches need a greater bunch of attention to follow, while pure deep learning counterpart is relatively simpler to get inside.

Machine Learning vs. Deep Learning in 5G Networks -- A Comparison of Scientific Impact

Introduction of fifth generation (5G) wireless network technology has matched the crucial need for high capacity and speed needs of the new generation mobile applications. Recent advances in Artificial Intelligence (AI) also empowered 5G cellular networks with two mainstreams as machine learning (ML) and deep learning (DL) techniques. Our study aims to uncover the differences in scientific impact for these two techniques by the means of statistical bibliometrics. The performed analysis includes citation performance with respect to indexing types, funding availability, journal or conference publishing options together with distributions of these metrics along years to evaluate the popularity trends in a detailed manner. Web of Science (WoS) database host 2245 papers for ML and 1407 papers for DL-related studies. DL studies, starting with 9% rate in 2013, has reached to 45% rate in 2022 among all DL and ML-related studies. Results related to scientific impact indicate that DL studies get slightly more average normalized citation (2.256) compared to ML studies (2.118) in 5G, while SCI-Expanded indexed papers in both sides tend to have similar citation performance (3.165 and 3.162 respectively). ML-related studies those are indexed in ESCI show twice citation performance compared to DL. Conference papers in DL domain and journal papers in ML domain are superior in scientific interest to their counterparts with minor differences. Highest citation performance for ML studies is achieved for year 2014, while this peak is observed for 2017 for DL studies. We can conclude that both publication and citation rate for DL-related papers tend to increase and outperform ML-based studies in 5G domain by the means of citation metrics.