Clustering-based link prediction in scientific coauthorship networks

2017 ◽  
Vol 28 (06) ◽  
pp. 1750082 ◽  
Author(s):  
Yang Ma ◽  
Guangquan Cheng ◽  
Zhong Liu ◽  
Xingxing Liang
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Clustering Method ◽  
Scientific Cooperation ◽  
Coauthorship Networks ◽  
Cooperation Network ◽  
Better Than

Link prediction in social networks has become a growing concern among researchers. In this paper, the clustering method was used to exploit the grouping tendency of nodes, and a clustering index (CI) was proposed to predict potential links with characteristics of scientific cooperation network taken into consideration. Results showed that CI performed better than the traditional indices for scientific coauthorship networks by compensating for their disadvantages. Compared with traditional algorithms, this method for a specific type of network can better reflect the features of the network and achieve more accurate predictions.

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.

scholarly journals Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches

2021 ◽  
Vol 13 (2) ◽  
pp. 1003
Author(s):  
Wei Chen ◽  
Hui Qu ◽  
Kuo Chi
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Geographical Location ◽  
Partner Selection ◽  
Basic Properties ◽  
Cooperation Network ◽  
The Common ◽  
Patent Applications ◽  
Selection For ◽  
Selection Of

To enhance competitiveness and protect interest, an increasing number of organizations cooperate on patent applications. Partner selection has attracted much more attention because it directly affects the success of patent cooperation. By collecting some cooperative patents applied for by different categories of organizations in China from 2007 to 2015, an interorganizational patent cooperation network was built for this paper. After analyzing certain basic properties of the network, it was found that the network possessed some typical characteristics of social networks. Moreover, the network could be divided into communities, and three communities were selected to analyze as representative. Furthermore, to explore the partner selection in the patent cooperation network, eight link prediction approaches commonly used in social networks were introduced to run on another interorganizational patent cooperation network built by the patents applied for in 2016. The precision metric results of the eight link prediction approaches show that they are effective in partnership prediction; in particular, the Common Neighbors (CN) index can be effectively applied to the selection of unfamiliar partners for organizations in patent cooperation. Moreover, this paper also verified the trust transitivity based not only on historical cooperation but also on geographical location, and the complementarity of capabilities still plays an important role in partner selection for organizations.

Application of Link Prediction in Temporal Networks

2013 ◽  
Vol 756-759 ◽  
pp. 2231-2236 ◽  
Author(s):  
Hai Hang Xu ◽  
Li Jun Zhang
Keyword(s):  
Link Prediction ◽  
Dynamic Network ◽  
Evolutionary Process ◽  
Citation Network ◽  
Network Dynamic ◽  
Temporal Networks ◽  
Active Factor ◽  
Cooperation Network ◽  
Structure Similarity ◽  
Better Than

Link prediction is an important research hotspot in complex networks.Correlational studies merely use static topology for prediction, without considering the influence of network dynamic evolutionary process on link prediction. We believe that the linksare derived from the evolutionary process of network, and dynamic network topology will contain more information, Moreover, many networks have time attribute naturally, which is apt to combine the similarity of time and structure for link prediction. The paper proposes the concept of active factor using time attribute, to extend the similaritybased link prediction framework.Thenmodeland analysis the data of citation network and cooperation network with temporal networks.Design the active factors for both network sand verify the performance of these new indexes. The results shows that the indexes with active factor perform better than structure similarity based indexes.

Conical SNA using fuzzy k-medoids based on user experience

2021 ◽  
pp. 002072092098849
Author(s):  
Poonam Rani ◽  
MPS Bhatia ◽  
Devendra K Tayal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
User Experience ◽  
Three Dimensional ◽  
Experimental Results ◽  
Dynamic Parameters ◽  
Clustering Method ◽  
Cone Model ◽  
Intelligent Approach ◽  
Conical Model

The paper presents an intelligent approach for the comparison of social networks through a cone model by using the fuzzy k-medoids clustering method. It makes use of a geometrical three-dimensional conical model, which astutely represents the user experience views. It uses both the static as well as the dynamic parameters of social networks. In this, we propose an algorithm that investigates which social network is more fruitful. For the experimental results, the proposed work is employed on the data collected from students from different universities through the Google forms, where students are required to rate their experience of using different social networks on different scales.

scholarly journals How Does Inter-Organizational Cooperation Impact Organizations’ Scientific Knowledge Generation? Evidence from the Biomass Energy Field

2020 ◽  
Vol 13 (1) ◽  
pp. 191
Author(s):  
Liu Li ◽  
Chaoying Tang
Keyword(s):  
Scientific Knowledge ◽  
Biomass Energy ◽  
Knowledge Generation ◽  
Degree Centrality ◽  
Technical Knowledge ◽  
Technical Cooperation ◽  
Scientific Cooperation ◽  
Structural Holes ◽  
Cooperation Network ◽  
Positive Effect

Previous studies have demonstrated that accessing external knowledge is important for organizations’ knowledge generation. The main purpose of this study is to investigate how the diversity and amount of organizations’ external scientific knowledge influence their scientific knowledge generation. We also consider the moderating effect of the redundant industrial scientific knowledge and the amount of technical knowledge from external technical cooperators. The social network analysis method is used to establish both ego- and industrial-scientific cooperation network, and ego-technical cooperation network in order to analyze the external scientific knowledge and technical knowledge. The empirical analysis is based on patent and article data of 106 organizations in the biomass energy industry (including firms, universities and research institutes), and the results show that organizations’ structural holes and degree centrality of scientific cooperation network have positive effects on their scientific knowledge generation. In addition, organizations’ degree centrality of technical cooperation network positively moderates the relationship between their degree centrality of scientific cooperation network and scientific knowledge generation. Furthermore, density of industrial scientific cooperation network decreases the positive effect of organizations’ structural holes on their scientific knowledge generation, while it strengthens the positive effect of degree centrality of scientific cooperation network on their scientific knowledge generation. Academic contributions and practical suggestions are discussed.

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