Comparison of online social relations in volume vs interaction

Chun, Hyonho; Kwak, Haewoon; Eom, Young-Ho; Ahn, Yong‐Yeol; Moon, Sang Heup; Jeong, Hawoong

doi:10.1145/1452520.1452528

Cited by 162 publications

(128 citation statements)

References 33 publications

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“…Our Renren dataset is larger than most previously studied OSN datasets, the exceptions being recent measurements of the Twitter network [4,16]. However, our dataset shares similar properties with prior studies [7,22,29]. This confirms that Renren is a representative social network, and the behavior of its users is likely to be indicative of users in other OSNs like Facebook.…”

Section: Social Graph Analysissupporting

confidence: 76%

“…The reciprocity coefficient of profile visits on Renren is only 0.23. In contrast, reciprocity of visible comments on Renren is 0.49, and the reciprocity of visible interactions on Cyworld [7] is 0.78. Compared to these visible interactions, latent interactions show much less reciprocity.…”

Section: Reciprocitymentioning

confidence: 90%

“…It is not surprising to see that average path length is [29], since those path lengths were computed over smaller subgraphs representing regional networks. It is surprising, however, when we compare it to results from Cyworld [7], since the two networks are similar in size and completeness. One reasonable explanation is that connectivity between Internet users in the Cyworld network is much stronger than connectivity between users in the Renren network.…”

Section: Social Graph Analysismentioning

confidence: 99%

“…Valafar et al characterize indirect fan-owner interactions via photos among users in Flickr [27]. Chun et al observe that visible interactions are almost bidirectional in Cyworld [7]. Wilson et al show the structure of the interaction graph differs significantly from the social network in Facebook [29].…”

Section: Related Workmentioning

confidence: 99%

“…To better understand the true nature of relationships between OSN users, more recent work has shifted focus to measuring observable social interactions [7,19,28,29]. By examining records of interaction events across different links, the studies distinguish close-knit, active relationships from weak or dormant relationships, and derive a more accurate predictive model for social behavior.…”

Section: Introductionmentioning

confidence: 99%

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Understanding latent interactions in online social networks

Jiang

Wilson

Wang

et al. 2010

Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement

152

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Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior, and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, passive actions such as profile browsing that cannot be observed by traditional measurement techniques.In this paper, we seek a deeper understanding of both visible and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 150 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed visitor logs for each user profile, and counters for each photo and diary/blog entry. We capture detailed histories of profile visits over a period of 90 days for more than 61,000 users in the Peking University Renren network, and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than visible events, non-reciprocal in nature, and that profile popularity are uncorrelated with the frequency of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior, and compare their structural properties against those of both visible interaction graphs and social graphs.

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Section: Social Graph Analysissupporting

confidence: 76%

Section: Reciprocitymentioning

confidence: 90%

Section: Social Graph Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding latent interactions in online social networks

Jiang

Wilson

Wang

et al. 2010

Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement

152

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Lustria

Kazmer

Glueckauf

et al. 2010

J. Am. Soc. Inf. Sci.

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Estimating user interaction strength in distributed online networks

Jia

Schoon

Pouwelse

et al. 2015

Concurrency and Computation

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SUMMARYUser interactions are indispensable for any online network to thrive, especially for BitTorrent-like and Web real-time communication-based distributed online networks that rely on users' collective contributions instead of the help of central servers. User interactions provide fine-grained information for many applications, such as security enhancement and cooperation promotion. To date, several schemes for estimating user interaction strength in centralized online networks have been proposed. In contrast, we present design, deployment, and analysis of UISE for user interaction strength estimation in distributed online networks. Among the strong points of UISE is that it captures both direct and indirect user interactions, and that it scales with only partial information dissemination. We apply UISE to devise the first distributed scheme for online time estimation and we implement it into Tribler, a distributed online network for media and social applications like file sharing, streaming, and voting. We demonstrate the accuracy and the scalability of UISE with different information dissemination protocols and user behaviors using simulations, emulations, and a real-world deployment.

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Comparison of online social relations in volume vs interaction

Cited by 162 publications

References 33 publications

Understanding latent interactions in online social networks

Understanding latent interactions in online social networks

Untitled

Estimating user interaction strength in distributed online networks

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