scholarly journals Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

2019 ◽  
Vol 5 ◽  
pp. e172 ◽  
Author(s):  
Ilya Makarov ◽  
Olga Gerasimova ◽  
Pavel Sulimov ◽  
Leonid E. Zhukov
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Research University ◽  
Binary Classification ◽  
Network Embedding ◽  
Russian Science ◽  
Additional Information ◽  
Network Representation ◽  
Russian Science Citation Index ◽  
Embedding Operator

We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

Knowledge management in the mirror of the Russian Scientific Citation Index

2021 ◽  
pp. 53-72
Author(s):  
Леонид Куприянович Бобров
Keyword(s):  
Knowledge Management ◽  
Research University ◽  
Web Of Science ◽  
Citation Index ◽  
Publication Activity ◽  
World Knowledge ◽  
Russian Science ◽  
Russian Scientific ◽  
Russian Science Citation Index ◽  
The World

В работе предпринята попытка наукометрического анализа развития области управления знаниями на материалах Российского индекса научного цитирования (РИНЦ). Динамика числа публикаций по управлению знаниями, содержащихся в РИНЦ, рассматривалась в сопоставлении с динамикой аналогичных публикаций в базах данных Web of Science, Scopus и ScienceDirect. Показано, что наблюдаемая положительная динамика мировой публикационной активности по проблемам управления знаниями и онтологическому проектированию коррелирует с динамикой развития мирового рынка управления знаниями. Статистика публикаций РИНЦ свидетельствует о том, что наиболее обсуждаемыми в научной печати являются решения с использованием онтологических моделей. Тенденция к снижению частоты встречаемости терминов управление знаниями и онтология в заглавиях публикаций и ключевых словах предположительно объясняется тем, что публикации по управлению знаниями и онтологиям становятся все более сфокусированными на решении технологических проблем, что влияет на терминологический состав заглавий и ключевых слов. На основе анализа данных РИНЦ построены топ-списки наиболее продуктивных организаций и авторов публикаций по управлению знаниями и онтологиям The aim of the paper is to analyze the publication activity of the Russian writing authors and research organizations published in the field of knowledge management. The study was analysed bibliometric methods, statistical analysis, and analytical tools provided by the Russian Science Citation Index (RSCI), SCOPUS, Web of Science, and Science Direct. Overall, the study accounts for 363695 SCOPUS documents, 111015 Web of Science documents, 41546 Science Direct documents, and 27072 RSCI documents. The dynamics of the number of publications in the RSCI was compared with the dynamics for Scopus, Web of Science, and Science Direct publications. It was found out that the positive dynamics of the world publication activity on knowledge management problems correlates with the dynamics of the world knowledge management market. It has been shown that the RSCI publications that use solutions based on the ontological models were among the most discussed. The greatest publication activity in the field of knowledge management and ontological design is demonstrated by Universities and research institutes located in Moscow and St. Petersburg, but also in Ulyanovsk, Samara, Yekaterinburg, Novosibirsk and Tomsk. The most cited authors are working in the A.P. Ershov Institute of Informatics Systems (Novosibirsk), the Samara National Research University, and the Ulyanovsk State Technical University. An analysis of the RSCI citation indexes have showed that the fundamental works of Russian scientists devoted to the scientific and methodological foundations of knowledge management and published in the period from 1992 to 2008 are still in demand today.

On Scientific Value of russian Journals in Economics and related fields

2013 ◽  
pp. 130-151 ◽  
Author(s):  
A. Muravyev
Keyword(s):  
Reference Lists ◽  
Citation Index ◽  
Economic Science ◽  
Detailed Data ◽  
Academic Quality ◽  
Russian Science ◽  
Russian Science Citation Index ◽  
Scientific Significance ◽  
Scientific Value

In this paper we attempt to classify Russian journals in economics and related disciplines for their scientific significance. We show that currently used criteria, such as a journal’s presence in the Higher Attestation Committee’s list of journals and the Russian Science Citation Index (RSCI) impact factor, are not very useful for assessing the academic quality of journals. Based on detailed data, including complete reference lists for 2010—2011, we find significant differentiation of Russian journals, including among those located at the top of the RSCI list. We identify two groups of Russian journals, tentatively called category A and B journals, that can be regarded as the most important from the viewpoint of their contribution to the economic science.

Scientometric Parameters of Russian Economic Science:General State and the Evaluation of Thesis boards

2013 ◽  
pp. 129-146 ◽  
Author(s):  
N. Kurakova ◽  
L. Tsvetkova ◽  
O. Eremchenko
Keyword(s):  
Science Citation Index ◽  
Web Of Science ◽  
Citation Index ◽  
Science Citation ◽  
Russian Science ◽  
Russian Science Citation Index

The paper analyses the publications of Russian authors in various fields of economics indexed in Web of Science and Russian Science Citation Index. The authors claim that the scientometric parameters are only in a limited way applicable in evaluating the performance of expert and thesis boards in economics in Russia. The authors also put forward the approaches in order to improve Russia’s positions in the international citations indexes in economics.

The experience of ranking russian economic journals

2015 ◽  
pp. 99-115 ◽  
Author(s):  
E. Balatsky ◽  
N. Ekimova
Keyword(s):  
Information System ◽  
Citation Index ◽  
Statistical Information ◽  
Expert Evaluation ◽  
Expert Interviews ◽  
Bibliometric Data ◽  
Russian Science ◽  
Ranking Systems ◽  
Economic Journals ◽  
Russian Science Citation Index

The article presents the results of the rating of Russian economic journals, the methodology of which is based on a combination of bibliometric data and expert interviews. Processing of the statistical information system of Russian science citation index (RINC) allows us to form a “primary” list of the best journals in the country. Expert evaluation of the list makes it possible to reorganize it with regard to the scientific level of periodicals and get the “secondary” list. The merger of two ranking systems forms the basis of obtaining the final ranking of economic journals. It is shown that the leading part of the constructed rating forms a kind of the Diamond List of journals, which on the whole agrees with similar lists obtained in earlier studies by other authors.

Journals’ hidden self-citation as the problem of publication ethics

2020 ◽  
Author(s):  
Nikolay Mazov ◽  
◽  
Vadim Gureyev ◽  
◽  
◽  
...  
Keyword(s):  
Science Citation Index ◽  
Web Of Science ◽  
Citation Index ◽  
Editorial Staff ◽  
Publication Activity ◽  
Publication Ethics ◽  
Russian Science ◽  
Russian Science Citation Index ◽  
Library Studies ◽  
First Time

Twenty two science Russian periodicals in informatics and library studies are selected for the bibliometrical analysis of key journal indicators, including publication activity of the same journals’ editorial staff. For the first time for domestic journals, the study reveals hidden self-citation when editorial members include links to their journal from other publications. The available instruments of scientometrical databases, including Web of Science and Scopus, and the national system Russian Science Citation Index do not enable to identify this form of self-citation. The mentioned manipulations are aimed at boosting journal rating. In several cases, intensive and unjustified citation by journals’ editorial staff in other periodicals which we consider the violation of publication ethical principles, is revealed.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

scholarly journals A hybrid recommender system based-on link prediction for movie baskets analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Randomized Algorithm ◽  
Similarity Criterion ◽  
Absolute Error ◽  
Similarity Criteria ◽  
Online Systems ◽  
Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Léo Pio-Lopez ◽  
Alberto Valdeolivas ◽  
Laurent Tichit ◽  
Élisabeth Remy ◽  
Anaïs Baudot
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Network Embedding ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Gene Associations ◽  
Different Types ◽  
Embedding Methods ◽  
Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

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