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We study the problem of multiple social network integration

COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,, pp.1485-1494, (2015)

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Keywords

Social networkNetwork integrationEnergy-based model

Abstract

More often than not, people are active in more than one social network. Identifying users from multiple heterogeneous social networks and integrating the different networks is a fundamental issue in many applications. The existing methods tackle this problem by estimating pairwise similarity between users in two networks. However, those m...More

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Introduction

The authors are facing an era of online with offline (OWO)—almost everyone is using online social networks to connect friends or, more generally, to satisfy social needs at different levels [25].
Many users participate in more than one social network, such as public networks and private networks, as well as business networks and family networks.
Preliminary statistics show that the average number of social networks in which a user participates is eight.
The intentions behind these choices are sophisticated.
Users generate heterogeneous content and build different ego-networks in different social networks.
One interesting and important question is: can the authors automatically integrate the different heterogeneous social networks together?

Highlights

We are facing an era of online with offline (OWO)—almost everyone is using online social networks to connect friends or, more generally, to satisfy social needs at different levels [25]
We propose COSNET (COnnecting heterogeneous Social NETworks with local and global consistency), a novel energy-based model, to address this problem by considering both local and global consistency among multiple networks
We demonstrate that applying the integration results produced by our method can improve the accuracy of expert finding, an important task in social networks
The SNS data collection consists of five popular online social networking sites: Twitter (TW), LiveJournal (LJ), Flickr (FL), Last.fm (LA), and MySpace (MS)
We study the problem of multiple social network integration
We develop an efficient model learning algorithm based on dual decomposition which can be parallelized

Methods

SVM: This method formalizes the matching problem as a classification problem
It trains a classification model based on the labeled data, and applies the classification model to classify whether a candidate matching is correct or not.
SiGMa [18]: This method was designed to align two knowledge bases, by propagating the confidence score in the matching graph.

Results

The authors perform experiments on two data collections: SNS and Academia4.
Each data collection consists of several social networks.
In the different networks of each data collection, both the users and the meanings of the relationships in the different networks are very different.
Table 3 lists statistics of the two data collections.
SNS Network Collection.
The SNS data collection consists of five popular online social networking sites: Twitter (TW), LiveJournal (LJ), Flickr (FL), Last.fm (LA), and MySpace (MS)

Conclusion

The authors study the problem of multiple social network integration.
The authors precisely define the problem, and propose a novel energy-based framework COSNET to address it.
The authors develop an efficient model learning algorithm based on dual decomposition which can be parallelized.
The authors' experimental results on two different genres of data sets validate the effectiveness and efficiency of the proposed framework.
The authors further validate the effectiveness of the method by applying the integrated results to support expert finding, an important application.
The authors thank Shlomo Berkovsky, Terence Chen, and Dali Kaafar for sharing the linked accounts data in this research

Tables

Table1: Notations. DESCRIPTION
Table2: Performance comparison of different methods for network integration task. The results are presented jointly and separately for each pair of networks
Table3: Statistics of the two datasets. SNS consists of five networks and Academia consists of three networks

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Related work

We briefly review related literature from two aspects: connecting users across social networks, and entity linking. Connecting Users across Social Networks. An immediate method to connect users in different networks is to leverage usernames [38, 27, 39, 22]. Zafarani et al [38] were the first to address this problem. Their approach utilized prefix/postfix addition and removal to map usernames from a base community to a target community. Peritio et al [27] estimated the uniqueness of usernames with a Markov chain model. The recent work by Zafarani et al [39] conducted a more in-depth investigation of this problem. They defined sophisticated features, such as knowledge limitation and typing patterns, to model the behavior patterns of users in selecting usernames. Liu et al [22] leveraged rare usernames to create training instances for user identification. Liu et al [23] explored the social identity linkage problem based on user behavior modeling. They proposed a method to incorporate user attributes, user generated content, and social activities to link user accounts in different social networks.

Funding

The work is supported by the National High-tech R&D Program (No 2014AA015103), National Basic Research Program of China (No 2014CB340506, No 2012CB316006), NSFC (No 61222212), NSFC-ANR (No 61261130588), National Social Science Foundation of China (No.13&ZD190), the Tsinghua University Initiative Scientific Research Program (20121088096), a research fund supported by Huawei Inc., and Beijing key lab of networked multimedia

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