Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

Ma, Jiangtao; Qiao, Yuanhua; Hu, Guangwu; Huang, Yudong; Wang, Meng; Sangaiah, Arun Kumar; Zhang, Chaoqin; Wang, Yanjun

doi:10.1109/access.2017.2717921

Cited by 27 publications

(25 citation statements)

References 32 publications

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“…Ma et al [61] presented an algorithm known as MapMe. The first step in this algorithm is to divide a big graph, which is a characteristic feature on most OSNs, into smaller subgraphs.…”

Section: ) Common Interest Among Users As a Featurementioning

confidence: 99%

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

et al. 2020

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Online Social Networks (OSNs) are an integral part of the modern lifestyle, and the use of OSN is highly prevalent today. OSNs have enabled the generation of unprecedented quantities of data on users, their interests, relationships, actions, opinions, locations visited, items purchased, and their experiences in various aspects of life. Data and information related to reputation and influence exist on OSNs and are hidden. The objective of this study is to provide a better and deeper understanding and a detailed review of the current state of research pertaining to the measurement of reputation and influence in OSNs, specifically Twitter. We conducted a Systematic Literature Review to collect, analyze, and synthesize data on the accuracy and value of previous literature that has focused on this field, has addressed a variety of topics, and has been published in digital databases between 2010 and September 2019. We followed a predefined review procedure that integrated both automatic and manual search strategies to cover all relevant research papers published in this period.

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“…Ma et al [61] presented an algorithm known as MapMe. The first step in this algorithm is to divide a big graph, which is a characteristic feature on most OSNs, into smaller subgraphs.…”

Section: ) Common Interest Among Users As a Featurementioning

confidence: 99%

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

et al. 2020

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“…Based on these open KGs, various industry KGs that are applicable to different industries (such as finance, agriculture, geography, meteorology, medicine and education) have also been constructed. These include the Gene Ontology [25] and Traditional Chinese Medicine KG [26] for biomedicine, Crop Diseases and Insect Pests [27] for agriculture, FOAF for social relations [28] and KGs for social networks [29], GeoNames Ontology and GeoKG for geospatial big data [30], [31], OSM Semantic Network [32]- [34], transportation KG [35], [36], KGs for multimedia conferencing process management [37], etc.…”

Section: B Knowledge Graph 1) Overview Of Knowledge Graphmentioning

confidence: 99%

Construction of Space Object Situation Information Service Based on Knowledge Graph

Lan

et al. 2020

IEEE Access

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To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multielement knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users' cognition of SOS knowledge.

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“…8,[17][18][19][24][25][26][27] While the others try to utilize multiple heterogeneous information for user account alignment, such as user the social relations, 6,7,9,10,[12][13][14][15]28 user interests, 4,7,10,29 user temporal distribution features. 4,6,11,28,30 To most of the studies on user account alignment, 4,8,[12][13][14][15][16][17]19,[24][25][26][27] the account name information is very important, since many users like to assign their accounts in different networks with very similar names, and the account names in most networks are very easy to be acquired. And how to properly utilize the name information in the alignment of accounts owned by the English users have already been well studied by many works, 8,16,17,19,24,26,27 among them: Vosecky et al …”

Section: Related Workmentioning

confidence: 99%

“…In recent years, many works have been proposed to align the accounts of common users across different social networks. 4,[7][8][9][10][11][12][13][14][15][16][17][18][19] And in most of these works, 4,8,[12][13][14][15][16][17][18][19] the account name information plays an important role, because:…”

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confidence: 99%

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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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Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

Cited by 27 publications

References 32 publications

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

Construction of Space Object Situation Information Service Based on Knowledge Graph

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

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