Predicting Social Links for New Users across Aligned Heterogeneous Social Networks

Online social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links among users are of great importance. Many existing link prediction methods focus on predicting social links that will appear in the future among all users based upon a snapshot of the social network. In real-world social networks, many new users are joining in the service every day. Predicting links for new users are more important. Different from conventional link prediction problems, link prediction for new users are more challenging due to the following reasons: (1) differences in information distributions between new users and the existing active users (i.e., old users); (2) lack of information from the new users in the network. We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts. We proposed a within-target-network personalized sampling method to process the existing active users' information in order to accommodate the differences in information distributions before the intra-network knowledge transfer. SCAN-PS can also exploit information in other source networks, where the user accounts are aligned with the target network. In this way, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network.Comment: 11 pages, 10 figures, 4 table

Predicting Anchor Links between Heterogeneous Social Networks

People usually get involved in multiple social networks to enjoy new services or to fulfill their needs. Many new social networks try to attract users of other existing networks to increase the number of their users. Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new inter-network link (called anchor link) is formed between the source and target networks. In this paper, we concentrated on predicting the formation of such anchor links between heterogeneous social networks. Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted, in anchor link prediction, the target user is missing and will be added to the target network once the anchor link is created. To solve this problem, we use meta-paths as a powerful tool for utilizing heterogeneous information in both the source and target networks. To this end, we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths (CRMP). By using those two different categories of meta-paths, we model different aspects of social factors that may affect a source user to join the target network, resulting in the formation of a new anchor link. Extensive experiments on real-world heterogeneous social networks demonstrate the effectiveness of the proposed method against the recent methods.Comment: To be published in "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Exploiting Social and Mobility Patterns for Friendship Prediction in Location-Based Social Networks

International audienceLink prediction is a " hot topic " in network analysis and has been largely used for friendship recommendation in social networks. With the increased use of location-based services, it is possible to improve the accuracy of link prediction methods by using the mobility of users. The majority of the link prediction methods focus on the importance of location for their visitors, disregarding the strength of relationships existing between these visitors. We, therefore, propose three new methods for friendship prediction by combining, efficiently, social and mobility patterns of users in location-based social networks (LBSNs). Experiments conducted on real-world datasets demonstrate that our proposals achieve a competitive performance with methods from the literature and, in most of the cases, outperform them. Moreover, our proposals use less computational resources by reducing considerably the number of irrelevant predictions, making the link prediction task more efficient and applicable for real world applications

Link Prediction with Personalized Social Influence

Link prediction in social networks is to infer the new links likely to be formed next or to reconstruct the links that are currently missing. Link prediction is of great interest recently since one of the most important goals of social networks is to connect people, so that they can interact with their friends from real world or make new friend through Internet. So the predicted links in social networks can be helpful for people to have connections with each others. Other than the pure topological network structures, social networks also have rich information of social activities of each user, such as tweeting, retweeting, and replying activities. Social science theories, such as social influence, suggests that the social activities could have potential impacts on the neighbors, and links in social networks are the results of the impacts taking place between different users. It motivates us to perform link prediction by taking advantage of the activity information. There has been a lot of proposed methods to measure the social influence through user activity information. However, traditional methods assigned some social influence measures to users universally based on their social activities, such as number of retweets or mentions the users have. But the social influence of one user towards others may not always remain the same with respect to different neighbors, which demands a personalized learning schema. Moreover, learning social influence from heterogeneous social activities is a nontrivial problem, since the information carried in the social activities is implicit and sometimes even noisy. Motivated by time-series analysis, we investigate the potential of modeling influence patterns based on pure timestamps, i.e., we aim to simplify the problem of processing heterogeneous social activities to a sequence of timestamps. Then we use timestamps as an abstraction of each activity to calculate the reduction of uncertainty of one users social activities given the knowledge of another one. The key idea is that, if a user i has impact on another user j, then given the activity timestamps of user i, the uncertainty in user j’s activity timestamps could be reduced. The uncertainty is measured by entropy in information theory, which is proven useful to detect the significant influence flow in time-series signals in information-theoretic applications. By employing the proposed influence metric, we incorporate the social activity information into the network structure, and learn a unified low-dimensional representation for all users. Thus, we could perform link prediction effectively based on the learned representation. Through comprehensive experiments, we demonstrate that the proposed method can perform better than the state-of-the-art methods in different real-world link prediction tasks

Survey on Link Prediction and Page Ranking In Blogs S.Geetha

This paper presents a study of the various aspects of link prediction and page ranking in blogs. Social networks have taken on a new eminence from the prospect of the analysis of social networks, which is a recent area of research which grew out of the social sciences as well as the exact sciences, especially with the computing capacity for mathematical calculations and even modelling which was previously impossible. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. Link prediction has many applications, including recommending new items in online networks (e.g., products in eBay and Amazon, and friends in Face book), monitoring and preventing criminal activities in a criminal network, predicting the next web page users will visit, and complementing missing links in automatic web data crawlers. Page Rank is the technique used by Google to determine importance of page on the web. It considers all incoming links to a page as votes for Page Rank. Our findings provide an overview of social relations and we address the problem of page ranking and link prediction in networked data, which appears in many applications such as network analysis or recommended systems. Keywords- web log, social networks analysis, readership, link prediction, Page ranking. I

Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

Link prediction methods and their accuracy for different social networks and network metrics

Currently, we are experiencing a rapid growth of the number of social–based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches that could provide a better prediction accuracy in social networked structures extracted from large–scale data about people and their activities and interactions, the existing methods are not comprehensively analysed. Presented in this paper, research focuses on the link prediction problem in which in a systematic way, we investigate the correlation between network metrics and accuracy of different prediction methods. For this study we selected six time–stamped real world social networks and ten most widely used link prediction methods. The results of our experiments show that the performance of some methods have a strong correlation with certain network metrics. We managed to distinguish ’prediction friendly’ networks, for which most of the prediction methods give good performance, as well as ’prediction unfriendly’ networks, for which most of the methods result in high prediction error. The results of the study are a valuable input for development of a new prediction approach which may be for example based on combination of several existing methods. Correlation analysis between network metrics and prediction accuracy of different methods may form the basis of a metalearning system where based on network characteristics and prior knowledge will be able to recommend the right prediction method for a given network at hand

A multilayer approach to multiplexity and link prediction in online geo-social networks.

Online social systems are multiplex in nature as multiple links may exist between the same two users across different social media. In this work, we study the geo-social properties of multiplex links, spanning more than one social network and apply their structural and interaction features to the problem of link prediction across social networking services. Exploring the intersection of two popular online platforms - Twitter and location-based social network Foursquare - we represent the two together as a composite multilayer online social network, where each platform represents a layer in the network. We find that pairs of users connected on both services, have greater neighbourhood similarity and are more similar in terms of their social and spatial properties on both platforms in comparison with pairs who are connected on just one of the social networks. Our evaluation, which aims to shed light on the implications of multiplexity for the link generation process, shows that we can successfully predict links across social networking services. In addition, we also show how combining information from multiple heterogeneous networks in a multilayer configuration can provide new insights into user interactions on online social networks, and can significantly improve link prediction systems with valuable applications to social bootstrapping and friend recommendations.This work was supported by the Project LASAGNE, Contract No. 318132 (STREP), funded by the European Commission and EPSRC through Grant GALE (EP/K019392).This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1140/epjds/s13688-016-0087-

Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs

Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy