scholarly journals Learning-based link prediction analysis for Facebook100 network

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Tim Poštuvan ◽  
Semir Salkić ◽  
Lovro Šubelj
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Network Science ◽  
Comprehensive Analysis ◽  
Machine Learning Algorithms ◽  
Similarity Metrics ◽  
Feature Sets ◽  
And Topology ◽  
Overall Performance ◽  
Prediction Techniques

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets. To derive the features, we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node- -based features, which are available for the Facebook100 network, though rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review the applied models, where overall performance and classification rates are presented.

A Novel Method to Dynamically Fix Threshold for Node Neighbourhood Based Link Prediction Techniques

2020 ◽  
Vol 15 (1) ◽  
pp. 17-34
Author(s):  
Anand Kumar Gupta ◽  
Neetu Sardana
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Binary Classification ◽  
Online Social Network ◽  
Threshold Value ◽  
Classification Problem ◽  
Similarity Score ◽  
The Social ◽  
Comparative Results ◽  
Prediction Techniques

The objective of an online social network is to amplify the stream of information among the users. This goal can be accomplished by maximizing interconnectivity among users using link prediction techniques. Existing link prediction techniques uses varied heuristics such as similarity score to predict possible connections. Link prediction can be considered a binary classification problem where probable class outcomes are presence and absence of connections. One of the challenges in classification is to decide threshold value. Since the social network is exceptionally dynamic in nature and each user possess different features, it is difficult to choose a static, common threshold which decides whether two non-connected users will form interconnectivity. This article proposes a novel technique, FIXT, that dynamically decides the threshold value for predicting the possibility of new link formation. The article evaluates the performance of FIXT with six baseline techniques. The comparative results depict that FIXT achieves accuracy up to 93% and outperforms baseline techniques.

scholarly journals A Comprehensive Survey of Link Prediction Techniques for Social Network

2020 ◽  
Vol 7 (23) ◽  
pp. 163988
Author(s):  
Abdul Samad ◽  
Mamoona Qadir ◽  
Ishrat Nawaz ◽  
Muhammad Islam ◽  
Muhammad Aleem
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Comprehensive Survey ◽  
Prediction Techniques

Structural Mining for Link Prediction Using Various Machine Learning Algorithms

2021 ◽  
Vol 12 (3) ◽  
pp. 66-78
Author(s):  
Ranjan Kumar Behera ◽  
Kshira Sagar Sahoo ◽  
Debadatt Naik ◽  
Santanu Kumar Rath ◽  
Bibhudatta Sahoo
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Performance Metrics ◽  
Similarity Index ◽  
Research Problem ◽  
Machine Learning Algorithms ◽  
Similarity Metrics ◽  
Proposed Model ◽  
The Future ◽  
Content Information

Link prediction is an emerging research problem in social network analysis, where future possible links are predicted based on the structural or the content information associated with the network. In this paper, various machine learning (ML) techniques have been utilized for predicting the future possible links based on the features extracted from the topological structure. Moreover, feature sets have been prepared by measuring different similarity metrics between all pair of nodes between which no link exists. For predicting the future possible links various supervised ML algorithms like K-NN, MLP, bagging, SVM, decision tree have been implemented. The feature set for each instance in the dataset has been prepared by measuring the similarity index between the non-existence links. The model has been trained to identify the new links which are likely to appear in the future but currently do not exist in the network. Further, the proposed model is validated through various performance metrics.

scholarly journals Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Jibouni Ayoub ◽  
Dounia Lotfi ◽  
Ahmed Hammouch
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Actual State ◽  
The Future ◽  
Auc Value ◽  
The Mean ◽  
Analysis Of Social Networks

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

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