Find me if You Can: Aligning Users in Different Social Networks

Online Social Networks allow users to share experiences with friends and relatives, make announcements, find news and jobs, and more. Several have user bases that number in the hundred of millions and even billions. Very often many users belong to multiple social networks at the same time under possibly different user names. Identifying a user from one social network on another social network gives information about a user\u27s behavior on each platform, which in turn can help companies perform graph mining tasks, such as community detection and link prediction. The process of identifying or aligning users in multiple networks is called network alignment. These similar (or same) users on different networks are called anchor nodes and the edges between them are called anchor links. The network alignment problem aims at finding these anchor links. In this work we propose two supervised algorithms and one unsupervised algorithm using thresholds. All these algorithms use local structural graph features of users and some of them use additional information about the users. We present the performance of our models in various settings using experiments based on Foursquare-Twitter and Facebook-Twitter data (User Identity Linkage Dataset). We show that our approaches perform well even when we use the neighborhood of the users only, and the accuracy improves even more given additional information about a user, such as the username and the profile image. We further show that our best approaches perform better at the HR@1 task than unsupervised and semi-supervised factoid embedding approaches considered earlier for these datasets

Risks of Friendships on Social Networks

In this paper, we explore the risks of friends in social networks caused by their friendship patterns, by using real life social network data and starting from a previously defined risk model. Particularly, we observe that risks of friendships can be mined by analyzing users' attitude towards friends of friends. This allows us to give new insights into friendship and risk dynamics on social networks.Comment: 10 pages, 8 figures, 3 tables. To Appear in the 2012 IEEE International Conference on Data Mining (ICDM

Job contact networks and the ethnic minorities

This paper examines the job finding methods of different ethnic groups in the UK. The theoretical framework shows that less assimilated ethnic unemployed workers are more likely to use their friends and family as their main method of search but they have less chance of finding a job using this method compared to whites and more assimilated ethnic unemployed workers that use formal job search methods (adverts, employment agencies etc.). Using data from the UK Quarterly Labour Force Survey (QLFS), we test these hypotheses. Our empirical findings are consistent with the theory since they suggest that, though networks are a popular method of finding a job for the ethnic minorities, they are not necessarily the most effective either in terms of gaining employment or in terms of the level of job achieved. However, there are important differences across ethnic groups with the Pakistani and Bangladeshi groups and those born outside the UK (the least assimilated), losing out disproportionately from using personal networks

A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201

The Strength of Internet Ties

Presents findings from a survey that examines how Americans use the Internet and email to support and expand their social networks and access resources for assistance in making major life decisions

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless P = NP. We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and per- form extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm

Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201