Integrated anchor and social link predictions across multiple social networks

Zhan, Qianyi; Zhang, Jiawei; Yu, Philip S.

doi:10.1007/s10115-018-1210-1

Cited by 50 publications

(64 citation statements)

References 29 publications

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“…Predicting multiple kinds of links among users across multiple aligned networks is de ned as the collective link prediction problem in [23]. e collective link prediction problem covers several di erent link formation prediction tasks simultaneously including both the intra-network social link prediction and the inter-network anchor link prediction.…”

Section: Introductionmentioning

confidence: 99%

Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention

Jiao

Xiong

Zhang

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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To enjoy more social network services, users nowadays are usually involved in multiple online sites at the same time. Aligned social networks provide more information to alleviate the problem of data insu ciency. In this paper, we target on the collective link prediction problem and aim to predict both the intra-network social links as well as the inter-network anchor links across multiple aligned social networks. It is not an easy task, and the major challenges involve the network characteristic di erence problem and di erent directivity properties of the social and anchor links to be predicted. To address the problem, we propose an application oriented network embedding framework, Hierarchical Graph A ention based Network Embedding (HGANE), for collective link prediction over directed aligned networks. Very di erent from the conventional general network embedding models, HGANE e ectively incorporates the collective link prediction task objectives into consideration. It learns the representations of nodes by aggregating information from both the intra-network neighbors (connected by social links) and inter-network partners (connected by anchor links). What's more, we introduce a hierarchical graph a ention mechanism for the intra-network neighbors and inter-network partners respectively, which resolves the network characteristic di erences and the link directivity challenges e ectively. Extensive experiments have been conducted on real-world aligned networks datasets to demonstrate that our model outperformed the state-of-the-art baseline methods in addressing the collective link prediction problem by a large margin.

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Section: Introductionmentioning

confidence: 99%

Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention

Jiao

Xiong

Zhang

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Self Cite

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“…By fusing multiple HINs, the heterogeneous information available in each network can be transferred to other aligned networks and lots of application problems on HIN, e.g., link prediction and friend recommendation [153], [151], community detection [154], information diffusion [155], will benefit from it a lot.…”

Section: G Information Fusionmentioning

confidence: 99%

“…Via the inferred mappings, Zhang et al propose to transfer heterogeneous links across aligned networks to improve quality of predicted links/recommended friends [153], [151]. For new networks [111] and new users [110] with little social activity information, the transferred information can greatly overcome the cold start problem when predicting links for them.…”

Section: G Information Fusionmentioning

confidence: 99%

“…For example, users usually co-exist in multiple social networks, such as Facebook, Google+, and Twitter. In this kind of applications, we need to align users in different networks and effectively fuse information from different networks [150], [151], [152]. More broadly, many networked data are difficult to be modeled with heterogeneous network with a simple network schema.…”

Section: ) Network Structurementioning

confidence: 99%

“…However, social networks in the real world are actually mostly partially aligned and lots of users are not anchor users. Zhang et al have proposed the partial network alignment methods based on supervised learning setting and PU learning setting in [150] and [151] respectively. In addition to these pairwise social network alignment problems, multiple (more than two) social networks can be aligned simultaneously.…”

Section: G Information Fusionmentioning

confidence: 99%

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A survey of heterogeneous information network analysis

Shi

Zhang

et al. 2017

IEEE Trans. Knowl. Data Eng.

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928

457

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Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions. Index Termsheterogeneous information network, data mining, semi-structural data, meta path

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

Zhu

Wang

Liu

et al. 2020

Concurrency and Computation

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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).

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Integrated anchor and social link predictions across multiple social networks

Cited by 50 publications

References 29 publications

Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention

Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention

A survey of heterogeneous information network analysis

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

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