Inferring anchor links across multiple heterogeneous social networks

IEEE Trans. Knowl. Data Eng.

Zhu

2015

Abstract-Social identity linkage across different social media platforms is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. In this paper, we propose a solution framework, HYDRA, which consists of three key steps: (I) we model heterogeneous behavior by long-term topical distribution analysis and multi-resolution temporal behavior matching against high noise and information missing, and the behavior similarity are described by multi-dimensional similarity vector for each user pair; (II) we build structure consistency models to maximize the structure and behavior consistency on users' core social structure across different platforms, thus the task of identity linkage can be performed on groups of users, which is beyond the individual level linkage in previous study; and (III) we propose a normalized-margin-based linkage function formulation, and learn the linkage function by multi-objective optimization where both supervised pair-wise linkage function learning and structure consistency maximization are conducted towards a unified Pareto optimal solution. The model is able to deal with drastic information missing, and avoid the curse-of-dimensionality in handling high dimensional sparse representation. Extensive experiments on 10 million users across seven popular social networks platforms demonstrate that HYDRA correctly identifies real user linkage across different platforms from massive noisy user behavior data records, and outperforms existing state-of-the-art approaches by at least 20% under different settings, and 4 times better in most settings.

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Structured Learning from Heterogeneous Behavior for Social Identity Linkage

IEEE Trans. Knowl. Data Eng.

Zhu

2015

“…Shehab [20] proposed a method leveraging the game appeal and social community to generate the profile mappings, in which procedure the game was modeled using incomplete information, and a proof of sequential equilibrium was given. Kong [2] formulated the inference problem for anchor links into a stable matching problem between the two sets of accounts in two different networks, who claimed that their methods can effectively predict the links between accounts. We applied stable matching model in our account matching procedure.…”

Section: Related Workmentioning

confidence: 99%

“…The two seemingly isolated web services have been connected by people: a great percentage of people have accounts of both social network and online shopping services. Matching a person's accounts in different services would be a crucial prerequisite for calculating many useful outcomes [2]. For example, it can be very profitable if we collect a person's profiles and actions in social networks to categorize or even predict the purchase behaviors of the same person in online shopping sites [3].…”

Section: Introductionmentioning

confidence: 99%

“…A bunch of solutions have been proposed to solve the problem, where most of them focus on the situation where account matching occurs between two homogeneous networks [2,4], such as the matching between Facebook and Twitter. Although the domains of the two comparing networks are not completely identical, most of them concern similar characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of purchase behaviors across heterogeneous social networks

et al. 2015

J Supercomput

Due to the development of web services, many social network sites, as well as online shopping sites have been booming in the past decade, where it is a common phenomenon that people are likely to use multiple services at the same time. On the one hand, previous research findings indicate the data sparsity issues of online shopping accounts, which is caused by the heavy-tailed distribution of user information. On the other hand, in social network sites, the personal information and the corresponding statuses of an account are abundant, and their genuineness is guaranteed either by the service provider, or by the willingness of the account owner to connect to his or her friends in reality. Making use of the correlation between accounts of a same individual is a crucial prerequisite for many interesting cross network applications, such as improving the recommendation performance of the online shopping sites using extra information from social network services. In this paper, we firstly propose a game-theoretic method to identify correlation accounts of individuals between social network sites and online shopping sites with stable matching model, incorporating account profiles as well as historical behaviors. Using the above account relationships, we then put forward a predicting method that combines heterogeneous social network information and online shopping information, to predict the purchasing behaviors. The results show that our method identifies up to 70 % of the correlation accounts between Facebook and eBay, one of the most popular social network sites and online shopping 123Prediction of purchase behaviors across heterogeneous... 3321 sites in the world, respectively. The experimental results also show that using the correlation account sets, the accuracy of our purchase predicting method outperforms the state-of-the-art methods by 5 %.

A multiview approach based on naming behavioral modeling for aligning chinese user accounts across multiple networks

Zhu

Concurrency and Computation

et al. 2020

Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter-network applications, for example, cross-network recommendation and cross-network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users' accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this article, we first discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Second, we propose the framework of Multi-View Cross-Network User Alignment (MCUA) method, which uses a multi-view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top-k valuable features of different types of name matchings for MCUA over our experimental datasets. K E Y W O R D S account name, aligning Chinese user accounts, multiview framework, multiple social networks 1 INTRODUCTION Online social networks are highly developed in recent years, 1 and hundreds of millions of Chinese people have become social network users. * Different social networks may provide different services, so it is natural for individuals to use multiple social networks for different purposes at the same time. 2 For example, a Chinese student may use Renren † to share funny photos with his classmates, use Sina Weibo ‡ to follow the latest events, and use Twitter § to connect with international friends. However, the accounts owned by the same user in different social sites are mostly isolated without any correspondence connections to each other. 3 Aligning the accounts of common users across different social networks is of great value to many concrete real-world internetwork applications. 3-6 For example, we can recommend new friends or new topics to a new Twitter user according to the social relationship information * Calculated by China Internet Network Information Center(CNNIC), the Statistics Report on China Internet Developing Situation(2015).