Recent development in the area of social networks has sought attention of the researchers to crunch and analyse the data and information of the users to retrieve relevant knowledge for further predictions and recommendations. Edge prediction is one such instance of social network analysis problem exploiting the prevailing data and information pertaining to the network such as: the attributes of the nodes and edges connecting the nodes in order to predict relationships potentially likely to exist in near future. Edge prediction has various applications in significant areas such as: knowledge mining, business recommendation systems, expert systems and bio informatics. In this work, we have classified the edge prediction problem in social network from five aspects: type of SN, feature used for edge prediction, edge prediction method, solution to edge prediction problem and performance measure. The strength of this article is the categorical review of the edge prediction methods in way to draw specific research problems to address further such as: complexity, accuracy, computational overhead and cost, scalability, generalization and performance issues. In addition to this, we have also provided an insightful of edge prediction method applied across various social network categories to understand the advantages and disadvantages to derive future work. The experimental exercise on real world social network particularly Face-book exhibits that the computation time taken in processing large network could be improved significantly may be through distributed techniques or so as the performance of edge prediction methods degrades with the scalability of the social networks. We did not focused upon any appropriate edge prediction methodology as it is out of the scope of the paper because we have exclusively reviewed the existing work done and we are exploring an appropriate ensemble method to precisely predict the future edges between nodes.

A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges

Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information network and citation network to predict the future links. Every network has got certain structural properties. For predicting the accuracy of future links, structural properties might play an important role. Current work is an effort to find out the correlation between structural properties of network and prediction accuracy. Positive Correlation will signify that knowing the structural properties of a Network will assist us to know the prediction accuracy in advance.

Significance of Clustering Coefficient over Jaccard Index

Path extension similarity link prediction method based on matrix algebra in directed networks

The Follower Link Prediction is an emerging application preferred by social networking sites to increase their user network. It helps in finding potential unseen individual and can be used for identifying relationship between nodes in social network. With the rapid growth of many users in social media, which users to follow leads to information overload problems. Previous works on link prediction problem are generally based on local and global features of a graph and limited to a smaller dataset. The number of users in social media is increasing in an extraordinary rate. Generating features for supervised learning from a large user network is challenging. In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem. Many hybrid graph feature techniques are used to represent the dataset suitable for machine learning. The efficiency of the LPXGB model is tested with three real world datasets Karate, Polblogs and Facebook. The proposed model is compared with various machine learning classifiers and also with traditional link prediction models. Experimental results are evident that the proposed model achieves higher classification accuracy and AUC value.

/pdf/follower-link-prediction-using-the-xgboost-classification-7w3rnkgasx.pdf

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Online social networking sites are a popular medium to exchange information among individuals. For measurement and analysis of social networking sites, we can assume it as a huge graph, where nodes represent users while edges represent links among users. Millions of nodes make the study of social network complex to analyze, so we narrow down by considering ego network rather than considering whole social network for our analysis. Ego network can be considered as an individual's own social network with their direct connections known as alters'. In this paper we are trying to identify the missing links among alters of an ego network with the help of Naive Bayes classification technique considering it as a link prediction technique. Link prediction techniques are used to predict the possibility of creation of new links in the network. For predicting new links, existing link prediction techniques calculate similarity score between two nodes in the network. Higher similarity score leads to the possibility of new links between nodes. These techniques mainly use common connectivity information, node degree or individual node features separately to calculate similarity score which may leads to high number of false predictions. Secondly deciding threshold for prediction is another issue with the similarity score based link prediction techniques. Thus we have implemented Naive Bayes approach by considering it as a binary classification problem to identify the missing links in an ego network. The proposed approach uses common neighbors and individual node features among its existing connections and based on that predicts the possibility of new links. We have validated the result with area under ROC curve.

Naïve Bayes Approach for Predicting Missing Links in Ego Networks

Link prediction techniques are used to identify the future network structure on the basis of existing connectivity pattern of the users. Most of the existing link prediction techniques employ varied similarity indices to predict new links in network. Some techniques use common neighbours while others use common shared profile information of the user for prediction. Typically existing link prediction techniques have only focused on one of these two data modalities: common neighbours or common attributes. Both of them play equally important role in the dynamics of the network. In this paper, we propose a feature integrated node neighbour (FINN) approach, an accurate algorithm for predicting links in network. FINN integrates Jaccard coefficient and Adamic Adar to predict link between nodes using their connections and features. We have evaluated FINN by implementing it over the real-time Facebook dataset collected from SNAP repository and validated the result through area under ROC curve.

Prediction of missing links in social networks: feature integration with node neighbour

Link prediction is an important task to identify the future interactions among existing non-connected members of a network, by measuring the proximity between nodes in a network. Link prediction is done on the basis of node similarity. Node neighbourhood based techniques are most popularly used for link prediction. Techniques based on Node neighbourhood can be applied on various applications like biological protein-protein interaction network, social network, information network and citation network to predict the future nodes. Every network has got certain topological properties. Topological properties play an integral role in choosing a technique for a particular network for predicting future links. This paper describes the impact of topological properties over link prediction technique. It also elaborates how presence of topological properties in a network improves the efficiency of the link prediction techniques.

Impact of topological properties over link prediction based on node neighbourhood: A study

Abstract Internet of Things (IoT) gadgets are very attractive globally, and IoT facilities are universal. Their progress has not been ignored, and the number of invaders and outbreaks on IoT and facilities is growing. Because cyber-assaults are zero-trust to the IoT, and the IoT is deeply ingrained in our lives and culture, we must embrace a comprehensive Zero-Trust strategy for cyber defense. As a result, the new model is no longer vulnerable to fear and outbreak on the IoT structure. In this article, an effort is made to categorize threat types and analyze and explain invaders and assaults on IoT gadgets and facilities.

Anand Kumar Gupta

Papers

Significance of Clustering Coefficient over Jaccard Index

Naïve Bayes Approach for Predicting Missing Links in Ego Networks

Prediction of missing links in social networks: feature integration with node neighbour

Impact of topological properties over link prediction based on node neighbourhood: A study

Categorizing threat types and cyber-assaults over Internet of Things-equipped gadgets