Citation count prediction as a link prediction problem

2015 ◽  
Vol 44 (2) ◽  
pp. 252-268 ◽  
Author(s):  
Nataliia Pobiedina ◽  
Ryutaro Ichise
Keyword(s):  
Link Prediction ◽  
Citation Count ◽  
Prediction Problem

Predicting Citation Count of Scientists as a Link Prediction Problem

2020 ◽  
Vol 50 (10) ◽  
pp. 4518-4529 ◽  
Author(s):  
Ertan Butun ◽  
Mehmet Kaya
Keyword(s):  
Link Prediction ◽  
Citation Count ◽  
Prediction Problem

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

scholarly journals Erratum: Corrigendum: Drug Response Prediction as a Link Prediction Problem

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zachary Stanfield ◽  
Mustafa Coşkun ◽  
Mehmet Koyutürk
Keyword(s):  
Link Prediction ◽  
Drug Response ◽  
Response Prediction ◽  
Prediction Problem

scholarly journals Drug Response Prediction as a Link Prediction Problem

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zachary Stanfield ◽  
Mustafa Coşkun ◽  
Mehmet Koyutürk
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Link Prediction ◽  
Drug Response ◽  
Response Prediction ◽  
Resistant Cell ◽  
Resistant Cell Line ◽  
Molecular Networks ◽  
Molecular Profile ◽  
Prediction Problem

Abstract Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.

A Network Structural Approach to the Link Prediction Problem

2015 ◽  
Vol 27 (2) ◽  
pp. 249-267 ◽  
Author(s):  
Chungmok Lee ◽  
Minh Pham ◽  
Myong K. Jeong ◽  
Dohyun Kim ◽  
Dennis K. J. Lin ◽  
...  
Keyword(s):  
Link Prediction ◽  
Structural Approach ◽  
Prediction Problem

scholarly journals Link Prediction in Social Networks Using Markov Random Field

2015 ◽  
Vol 37 ◽  
pp. 125 ◽  
Author(s):  
Zohreh Zalaghi
Keyword(s):  
Social Networks ◽  
Random Field ◽  
Graphical Models ◽  
Markov Random Field ◽  
Link Prediction ◽  
Prediction Problem ◽  
Binary Classifier ◽  
Markov Random ◽  
Networks Analysis ◽  
Better Than

Link prediction is an important task for social networks analysis, which also has applications in other domains such as information retrieval, recommender systems and e-commerce. The task is related to predicting the probable connection between two nodes in the netwok. These links are subjected to loss because of the improper creation or the lack of reflection of links in the networks; so it`s possible to develop or complete these networks and recycle the lost items and information through link prediction. In order to discover and predict these links we need the information of the nodes in the network. The information are usually extracted from the network`s graph and utilized as factors for recognition. There exist a variety of techniques for link prediction, amongst them, the most practical and current one is supervised learning based approach. In this approach, the link prediction is considered as binary classifier that each pair of nodes can be 0 or 1. The value of 0 indicates no connection between nodes and 1 means that there is a connection between them. In this research, while studying probabilistic graphical models, we use Markov random field (MRF) for link prediction problem in social networks. Experimentl results on Flicker dataset showed the proposed method was better than previous methods in precision and recall.

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