Affinity Prediction in Online Social Networks

International Journal of Advanced Computer Science and Applications, 2018

The unparalleled accomplishment of social networking sites, such as Facebook, LinkedIn and Twitter has modernized and transformed the way people communicate to each other. Nowadays, a huge amount of information is being shared by online users through these social networking sites. Various online friendship sites such as Facebook and Orkut, allow online friends to share their thoughts or opinions, comment on others' timeline or photos, and most importantly, meet new online friends who were known to them before. However, the question remains as to how to quickly propagate one's online network by including more and more new friends. For this, one of the easy methods used is list of 'Suggested Friends' provided by these online social networking sites. For suggestion of friends, prediction of links for each online user is needed to be made based on studying the structural properties of the network. Link prediction is one of the key research directions in social network analysis which has attracted much attention in recent years. This paper discusses about a novel efficient link prediction technique LinkGyp and many other commonly used existing prediction techniques for suggestion of friends to online users of a social network and also carries out experimental evaluations to make a comparative analysis among each technique. Our results on three real social network datasets show that the novel LinkGyp link prediction technique yields more accurate results than several existing link prediction techniques.

2012 Ninth International Conference on Information Technology - New Generations, 2012

Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the "algorithmic small world hypothesis". As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in all data sets. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability.

IEICE Transactions on Information and Systems, 2012

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

ACM Transactions on the Web

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community enhanced framework is presented in this paper to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using par...

International Journal of Information Technology and Web Engineering, 2020

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 comparativ...

Online network systems have become popular in many social, biological and information system in recent year. Much research has been done in social network, which is increasingly growing and form a large complex network. Social network such as Facebook provides a platform for users to share their interest, photos & post etc with their friend. A social network can be well described by a network graph, in which node represents users and edges between nodes represent some association. In most social network links are dynamics and change over the time in network. To predict an association between two nodes in a graph, which is likely to be occur in near future and is termed as link prediction. Many approach and method have been used for predicting a link in past years, a significant interest of the methods uses local and global structure of the graph to make predictions. In this survey we are highlighting the impact of time in association (collaboration or interaction) & temporal behavio...

International Journal of Electrical and Computer Engineering (IJECE), 2022

In recent years, the study of social networks and the analysis of these networks in various fields have grown significantly. One of the most widely used fields in the study of social networks is the issue of link prediction, which has recently been very popular among researchers. A link in a social network means communication between members of the network, which can include friendships, cooperation, writing a joint article or even membership in a common place such as a company or club. The main purpose of link prediction is to investigate the possibility of creating or deleting links between members in the future state of the network using the analysis of its current state. In this paper, three new similarities, degree neighbor similarity (DNS), path neighbor similarity (PNS) and degree path neighbor Similarity (DPNS) criteria are introduced using neighbor-based and path-based similarity criteria, both of which use graph structures. The results have been tested based on area under ...

Proceedings of the 2014 CoNEXT on Student Workshop, 2014

The problem of link prediction has gained a lot of attention recently from the research community. It can be formalized as, given a snapshot of a social network at time t, can it be predicted which new connections among its members are likely to occur in the future at time t. Apart from analysing social networks, it has also found application in other domains e.g., information retrieval, bio-informatics and e-commerce. Topological information of the network, i.e. the information about the present nodes and links, can be used to predict the future links in the network. As an example, "Common neighbors" method is a trivial but efficient strategy for predicting the possibility of a link between a pair of nodes. Many variants of the common neighbors method have been proposed to address this problem. In this paper, we propose a novel strategy for predicting the missing links which also takes into account the number of links between two sets of uncommon neighbors of given nodes, in addition to their common neighbors.

Given a snapshot of a social network, can we infer which new interactions among its members are likely to happen in near future? We formalize this question as the link prediction problem and develop approaches to link prediction based on the measures for analyzing the "vicinity" of the nodes participating in the network. In addition to that can we further determine the type of link between two nodes? We start discussing this issue in which the social network can have positive (indicating relations such as friendship) or negative (indicating relations such as opposition/antagonism) relationships.