Comparitive Analysis of Link Prediction in Complex Networks

2021 ◽  
Vol 12 (3) ◽  
pp. 44-60
Author(s):  
Furqan Nasir ◽  
Haji Gul ◽  
Muhammad Bakhsh ◽  
Abdus Salam
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Complex Networks ◽  
Complex Network ◽  
Link Prediction ◽  
Jaccard Coefficient ◽  
Network Link ◽  
Multiple Prediction ◽  
Prediction Techniques ◽  
Link Formation

The most attractive aspect of data mining is link prediction in a complex network. Link prediction is the behavior of the network link formation by predicting missed and future relationships among elements based on current observed connections. Link prediction techniques can be categorized into probabilistic, similarity, and dimension reduction based. In this paper six familiar link predictors are applied on seven different network datasets to provide directory to users. The experimental results of multiple prediction algorithms were compared and analyzed on the basis of proposed comparative link prediction model. The results revealed that Jaccard coefficient and Hub promoted performed well on most of the datasets. Different applied methods are arranged on the basis of accuracy. Moreover, the shortcomings of different techniques are also presented.

Relative Assortativity Index: A Quantitative Metric to Assess the Impact of Link Prediction Techniques on Assortativity of Complex Networks

2019 ◽  
Vol 63 (9) ◽  
pp. 1417-1437
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Biological Networks ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Prediction Technique ◽  
Prediction Techniques ◽  
The Impact

Abstract We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

scholarly journals Combining complex networks and data mining: why and how

2016 ◽  
Author(s):  
M. Zanin ◽  
D. Papo ◽  
P. A. Sousa ◽  
E. Menasalvas ◽  
A. Nicchi ◽  
...  
Keyword(s):  
Data Mining ◽  
Complex Networks ◽  
Complex Network ◽  
Network Theory ◽  
Data Sets ◽  
Complex Network Theory ◽  
Data Mining Algorithms ◽  
Starting Point ◽  
The Face ◽  
General Goal

AbstractThe increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

Road network link prediction model based on subgraph pattern

2020 ◽  
Vol 31 (06) ◽  
pp. 2050083
Author(s):  
Bin Wang ◽  
Xiaoxia Pan ◽  
Yilei Li ◽  
Jinfang Sheng ◽  
Jun Long ◽  
...  
Keyword(s):  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Road Network ◽  
Network Embedding ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Network Link ◽  
Subgraph Pattern

Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery

2016 ◽  
Vol 55 (04) ◽  
pp. 340-346 ◽  
Author(s):  
Thomas Rindflesch ◽  
Dimitar Hristovski ◽  
Andrej Kastrin
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Mesh Network ◽  
Jaccard Coefficient ◽  
Common Neighbor ◽  
Mesh Terms ◽  
Unsupervised Approach ◽  
Proximity Measures ◽  
Prediction Approach ◽  
Link Formation

Summary Objectives:Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts. Methods:We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future. Results:Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76),gfollowed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naïve Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87). Conclusions:The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.

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