Supervised Link Prediction Using Forecasting Models on Weighted Online Social Network

2016 ◽  
pp. 249-261 ◽  
Author(s):  
Anshul Gupta ◽  
Shalki Sharma ◽  
Hirdesh Shivhare
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Online Social Network ◽  
Forecasting Models

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.

Similarity-Based Indices or Metrics for Link Prediction

2019 ◽  
pp. 1-29
Author(s):  
Praveen Kumar Bhanodia ◽  
Kamal Kumar Sethi ◽  
Aditya Khamparia ◽  
Babita Pandey ◽  
Shaligram Prajapat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Link Prediction ◽  
Online Social Network ◽  
Dynamic Networks ◽  
Relevant Information ◽  
Network Evolution ◽  
Problem Instance ◽  
The Social ◽  
Social Network Evolution

Link prediction in social network has gained momentum with the inception of machine learning. The social networks are evolving into smart dynamic networks possessing various relevant information about the user. The relationship between users can be approximated by evaluation of similarity between the users. Online social network (OSN) refers to the formulation of association (relationship/links) between users known as nodes. Evolution of OSNs such as Facebook, Twitter, Hi-Fi, LinkedIn has provided a momentum to the growth of such social networks, whereby millions of users are joining it. The online social network evolution has motivated scientists and researchers to analyze the data and information of OSN in order to recommend the future friends. Link prediction is a problem instance of such recommendation systems. Link prediction is basically a phenomenon through which potential links between nodes are identified on a network over the period of time. In this chapter, the authors describe the similarity metrics that further would be instrumental in recognition of future links between nodes.

Homophily-Based Link Prediction in The Facebook Online Social Network: A Rough Sets Approach

2015 ◽  
Vol 24 (4) ◽  
pp. 491-503 ◽  
Author(s):  
Islam Elkabani ◽  
Roa A. Aboo Khachfeh
Keyword(s):  
Logistic Regression ◽  
Social Network ◽  
Decision Tree ◽  
Rough Set ◽  
Link Prediction ◽  
Online Social Network ◽  
Support Vector ◽  
Jaccard Coefficient ◽  
The Common ◽  
Overlap Coefficient

AbstractOnline social networks are highly dynamic and sparse. One of the main problems in analyzing these networks is the problem of predicting the existence of links between users on these networks: the link prediction problem. Many studies have been conducted to predict links using a variety of techniques like the decision tree and the logistic regression approaches. In this work, we will illustrate the use of the rough set theory in predicting links over the Facebook social network based on homophilic features. Other supervised learning algorithms are also employed in our experiments and compared with the rough set classifier, such as naive Bayes, J48 decision tree, support vector machine, logistic regression, and multilayer perceptron neural network. Moreover, we studied the influence of the “common groups” and “common page likes” homophilic features on predicting friendship between users of Facebook, and also studied the effect of using the Jaccard coefficient in measuring the similarity between users’ homophilic attributes compared with using the overlap coefficient. We conducted our experiments on two different datasets obtained from the Facebook online social network, where users in each dataset live within the same geographical region. The results showed that the rough set classifier significantly outperformed the other classifiers in all experiments. The results also demonstrated that the common groups and the common page likes features have a significant influence on predicting the friendship between users of Facebook. Finally, the results revealed that using the overlap coefficient homophilic features provided better results than that of the Jaccard coefficient features.

Sign in / Sign up

Export Citation Format

Share Document