Predicting Citation Counts for Academic Literature Using Graph Pattern Mining

Pobiedina, Nataliia; Ichise, Ryutaro

doi:10.1007/978-3-319-07467-2_12

Cited by 15 publications

(3 citation statements)

References 10 publications

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“…This score has proven to perform well not only for link prediction in social networks [3], but also for prediction of citation counts over time [14].…”

Section: Time and Heterogeneity Based Scoresmentioning

confidence: 98%

Exploring Supervised Methods for Temporal Link Prediction in Heterogeneous Social Networks

Rümmele

Ichise

Werthner

2015

Proceedings of the 24th International Conference on World Wide Web

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In the link prediction problem, formulated as a binary classification problem, we want to classify each pair of disconnected nodes in the network whether they will be connected by a link in the future. We study link formation in social networks with two types of links over several time periods. To solve the link prediction problem, we follow the approach of counting 3-node graphlets and suggest three extensions to the original method. By performing experiments on two real-world social networks we show that the new methods have a predictive power, however, network evolution cannot be explained by one specific feature at all time points. We also observe that some network properties can point at features which are more effective for temporal link prediction.

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“…This score has proven to perform well not only for link prediction in social networks [3], but also for prediction of citation counts over time [14].…”

Section: Time and Heterogeneity Based Scoresmentioning

confidence: 98%

Exploring Supervised Methods for Temporal Link Prediction in Heterogeneous Social Networks

Rümmele

Ichise

Werthner

2015

Proceedings of the 24th International Conference on World Wide Web

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“…Pobiedina ve Ichise [13] bir atıf ağı oluşturmuş ve öznitelik yaratmak için gelen bağlantılar (in-degree) ile giden bağlantıların (out-degree) sayılarını kullanmışlardır. İki adet gerçek veri kümesi üzerinde Lojistik Regresyon (Logistic Regression), Destek Vektör Makineleri (Support Vector Machines) ve Ağaçlar (Trees) tekniklerini uygulayarak bir yıllık tahmin yapmışlardır.…”

Section: Avrupa Bilim Ve Teknoloji Dergisi E-issn: 2148-2683 372unclassified

Bilimsel Makalelerin Atıf Sayısı Tahmini

Kizilöz¹

2020

European Journal of Science and Technology

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ÖzBilimsel makalelerin etkisini ölçmek kolay ya da tekdüze bir süreç değildir. Makalelerin atıf sayıları, etkilerinin ölçümünde önemli bir rol oynamaktadır. Öte yandan, bir makalenin atıf sayısı, makale yayınlandığı anda elde edilebilen bir veri değildir. Atıf sayısının elde edilebilmesi için makalenin yayınlanması ve toplulukta fark edilerek atıf(lar) alması, yani uzun sayılabilecek bir süre geçmesi gerekmektedir. Bu çalışmada, atıf sayısının erişilebilir olmaması problemini basitleştirdik ve bir makalenin yayınlanmasından sonraki bir yıl içerisinde en az bir atıf alıp almayacağını tahmin eden bir derin öğrenme modeli oluşturduk. Modelimizde kelime dizileri arasındaki ilişkiyi bulabilmek adına Uzun Kısa Süreli Bellek (UKSB) kullanılmaktadır. Bunun yanı sıra, bu çalışmada modelimizin makale tam metni yerine sadece özetini kullandığımızda bu durumun performans üzerindeki etkisini de analiz ediyoruz. Deneylerimizde herkese açık veri kümelerini kullanılmıştır. Makalelerin tam metni Kaggle'da bulunan bir veri kümesinde mevcuttur. Özet, üstveri öznitelikleri ve ilk yıl atıf sayıları ise Microsoft Academic Graph'tan çıkarılmıştır. Elde edilen sonuçlar, tam metin kullanımının daha yüksek doğrulukla sonuçlandığını göstermektedir. Fakat tam metin kullanıldığında modelin eğitim süresi, özet kullanıldığındaki eğitim süresine göre çok yüksek çıkmaktadır. Ayrıca, tam metinlere kıyasla makale özetleri daha kolay erişilebilir durumdadır. Son olarak, eğittiğimiz model bu makalenin ilk yayın yılında en az bir atıf alacağını öngörmektedir.

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“…A citation network is proposed for temporal ordering [37] of documents to detect topic evolution and embryonic trends from data and formerly use citations to calculate the loads for the key terms in papers. Research papers' data is synchronized to a classification of areas built on the important words from the titles and abstracts and is studied to capture the variations in the number of publications linked with such topics using a citation network [16].…”

Section: Related Workmentioning

confidence: 99%

Impact of Field of Study Trend on Scientific Articles

Zafar

Masood

2020

IEEE Access

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The volume and diversity of scientific literature have been increasing day by day and millions of new scientific articles are published each year. Researchers work and publish in their respective fields of interest. A major portion of the scientific community publishing in the same field of interest forms a trend in the field which could be deemed as relatively more popular than other trends. A novice researcher chooses his field of interest based upon its popularity. This may have a positive impact on the acceptance of a study or high count of citations in future. This study identifies how significant it is to follow a research trend and the impact of the field of study (FoS) trend on research paper citations. For this purpose, we have chosen the field of Computer Science and Microsoft Academic Graph (MAG) dataset from the 2007 to 2015 time period. In the dataset, every paper has a list of fields of study. The FoS provided in MAG is systematized hierarchically into 4 levels; level-0level-3. In this study, we have applied the clustering technique to the FoS and citations pattern separately. Likewise, we also analyzed how papers following a FoS trend, gain citations over the time. We have also introduced a novel method Field of Study Multigraph (FoM) using graph centrality measures degree, betweenness and closeness to analyze the FoS trend, citation trend, and the relation between research areas in scientific articles from the domain of Computer Science. The experimental results show that the FoS has some impact on citation count. Furthermore, the results depict that if papers belong to the same FoS, then there are 66% of the chances of having a similar citation pattern and that they have the same citation trend as they also have achieved a high correlation value. This proves that FoS has a certain impact on the citation count of a paper and researchers should contemplate on the FoS trend before selecting a particular research area.

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Predicting Citation Counts for Academic Literature Using Graph Pattern Mining

Abstract: the University of Greenwich open access repository http://gala.gre.ac.uk

Cited by 15 publications

References 10 publications

Exploring Supervised Methods for Temporal Link Prediction in Heterogeneous Social Networks

Exploring Supervised Methods for Temporal Link Prediction in Heterogeneous Social Networks

Bilimsel Makalelerin Atıf Sayısı Tahmini

Impact of Field of Study Trend on Scientific Articles

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