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

The growth of social networks has lately attracted both academic and industrial researchers to study the ties between people, and how the social networks evolve with time. Social networks like Facebook, Twitter and Flickr require efficient and accurate methods to recommend friends to their users in the network. Several algorithms have been developed to recommend friends or predict likelihood of future links. Two main approaches are used to utilize those features; Score-based Approaches and Machine Learning Approaches. In a previous work, a score-based method was used based on topological, node and social features to calculate similarity between users and determine the likelihood of forming future links. This work has been extended by moving to a Machine Learning Approach which treats the prediction process as a classification problem. The classifier predicts the class of each edge whether it exists or doesn’t exist. Machine Learning Approaches have the benefit of adding all similarity indices needed as the feature set fed to the classifier. While in Score-based Approach when we used multiple features with associated weights, the performance was sensitive to the values of such weights. When machine learning is applied, the learning process is performed by the classifier which is fed by eight similarity indices representing connectivity, community, interaction and trust in social network. When indices are combined, a much higher accuracy than the previous Score-based Approach is obtained and hence enhancing the prediction accuracy. In order to evaluate the correctness of the proposed model, it has been applied on a real dataset of 2.974k users on the Twitter social network. Experiments show that using both classical and ensemble classifiers outperforms baseline algorithms when applied individually.

A supervised learning approach to link prediction in Twitter

In recent years, multiplex networks have been introduced to describe real complex systems, where the same group of entities make different types of interaction In a multiplex network, each layer expresses one distinct type of interaction Link prediction is a research hotspot in complex network analysis A large number of link prediction methods have been proposed, but only a few were designed for multiplex networks In this paper, we focus on the link prediction problem in multiplex networks In our opinion, an approach in which link prediction is performed by simultaneously considering the information from all layers is advisable, because the formation of links in one layer can be affected by links of the same node pairs in other layers A supervised method is proposed in this study to implement link prediction in multiplex networks, which regards link prediction as a binary classification problem In the proposed method, a classification model is fed by a set of elaborate structural features of node pairs that are extracted from all layers Extensive experiments are conducted on six networks to analyze the effectiveness of the proposed method The results demonstrate that the proposed method outperforms the compared methods significantly

Supervised link prediction in multiplex networks

Evidential link prediction in social networks based on structural and social information

Microblogs data is the microlength user-generated data that is posted on the web, e.g., tweets, online reviews, comments on news and social media. It has gained considerable attention in recent years due to its widespread popularity, rich content, and value in several societal applications. Nowadays, microblogs applications span a wide spectrum of interests including targeted advertising, market reports, news delivery, political campaigns, rescue services, and public health. Consequently, major research efforts have been spent to manage, analyze, and visualize microblogs to support different applications. This paper gives a comprehensive review of major research and system work in microblogs data management. The paper reviews core components that enable large-scale querying and indexing for microblogs data. A dedicated part gives particular focus for discussing system-level issues and on-going effort on supporting microblogs through the rising wave of big data systems. In addition, we review the major research topics that exploit these core data management components to provide innovative and effective analysis and visualization for microblogs, such as event detection, recommendations, automatic geotagging, and user queries. Throughout the different parts, we highlight the challenges, innovations, and future opportunities in microblogs data research.

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Microblogs data management: a survey

Studying social networks and the ties connecting people in those networks has attracted many researchers. Social networks like Facebook, Twitter and Flickr require efficient and accurate methods to recommend friends to their users in the network. Several algorithms have been developed to recommend friends or predict likelihood of future links. Link Prediction algorithms utilize local features of the network in the neighborhood of the two nodes in question, or use global features like path structure of the whole network. New algorithms tend to combine both in order to achieve the best results such as FriendTNS that takes into account the degrees of the nodes, and the direct links between them. This paper extends FriendTNS such that it takes the strength of the tie between two users into account. The strength of the tie is represented by the interaction that takes place between two users. In order to evaluate the correctness of the proposed model, it has been applied on a real dataset of 2.974k users on the Twitter social network. The proposed model considers different features of users to represent their connective and social relationships. Experiments shows that the proposed model outperforms traditional algorithms when applied individually.

Cherry Ahmed

Papers

A supervised learning approach to link prediction in Twitter

Enhancing Link Prediction in Twitter using Semantic User Attributes