Growth of the flickr social network

Mislove, Alan; Koppula, Hema Swetha; Gummadi, Krishna P.; Druschel, Peter; Bhattacharjee, Bobby

doi:10.1145/1397735.1397742

Cited by 361 publications

(246 citation statements)

References 8 publications

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“…Some of them focus on static properties by looking at data sets collected at a single time point [2,3,25], while others investigate dynamic properties from a series of data sets over time [18,20,26]. These studies found a collection of remarkable properties such as the power-law scaling characteristics, the small-world phenomena, and clustered community structures.…”

Section: Osn Measurementsmentioning

confidence: 99%

Measurement-calibrated graph models for social network experiments

Sala

Cao

Wilson

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

146

127

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Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search. Due to the high time and resource costs of gathering real graph datasets through direct measurements, researchers are anonymizing and sharing a small number of valuable datasets with the community. However, performing experiments using shared real datasets faces three key disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large datasets, and that a small number of available social graphs limits the statistical confidence in the results.The use of measurement-calibrated graph models is an attractive alternative to sharing datasets. Researchers can "fit" a graph model to a real social graph, extract a set of model parameters, and use them to generate multiple synthetic graphs statistically similar to the original graph. While numerous graph models have been proposed, it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs. In this paper, we explore the feasibility of measurement-calibrated synthetic graphs using six popular graph models and a variety of real social graphs gathered from the Facebook social network ranging from 30,000 to 3 million edges. We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs. However, only one produces high fidelity results in our application-level benchmarks. While this shows that graph models can produce realistic synthetic graphs, it also highlights the fact that current graph metrics remain incomplete, and some applications expose graph properties that do not map to existing metrics.

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Section: Osn Measurementsmentioning

confidence: 99%

Measurement-calibrated graph models for social network experiments

Sala

Cao

Wilson

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

146

127

View full text Add to dashboard Cite

show abstract

“…We model the growth dynamics in an OSN (i.e. joining of new users, creation of new social links) by the preferential attachment model [4] which has been experimentally shown to occur in several OSNs [13,15]. Also, it produces power-law degree distributions similar to the empirical distributions in Twitter before the restriction was imposed [10,12].…”

Section: Modeling Restricted Growth Dynamics Of Osnsmentioning

confidence: 99%

“…The denominator in (1), which needs to be evaluated for only those nodes that are currently not blocked by the restriction (i.e. (i, j)-nodes for which β i,j+1 = 1), is in fact (15) where ζ = j (j + μ)c j is the unknown term. Thus the denominator of the second fraction on the right-hand side in (7) should actually be (1 + μp − ζ).…”

Section: Effects Of Restrictions On Degree Distributionsmentioning

confidence: 99%

“…There have been several studies on the topological characteristics that emerge as a result of various growth dynamics in OSNs [5,13,15]; however, to the best of our knowledge, ours is the first set of work on analysing the effects of restrictions on node-degree on these dynamics. From a network science perspective, though there have been studies on the effects of 'hard' cut-offs on node-degree (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Effects of a Soft Cut-Off in the Twitter Social Network

Ghosh

Srivastava

Ganguly

2011

Lecture Notes in Computer Science

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Abstract. Most popular OSNs currently restrict the number of social links that a user can have, in order to deal with the problems of increasing spam and scalability in the face of a rapid rise in the number of users in recent years. However such restrictions are often being criticized by socially active and popular users, hence the OSN authorities are facing serious design-choices while imposing restrictions; this is evident from the innovative 'soft' cut-off recently imposed in Twitter instead of the traditional 'hard' cut-offs in other OSNs. Our goal in this paper is to develop an analytical framework taking the restriction in Twitter as a case-study, that can be used to make proper design-choices considering the conflicting objectives of reducing system-load and minimizing userdissatisfaction. We consequently define a simple utility function considering the above two objectives, and find that Twitter's policy well balances both. From a network science perspective, this is the first analysis of 'soft' cut-offs in any sort of network, to the best of our knowledge.

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“…Ahn et al analyze topological characteristics of Cyworld, MySpace and Orkut [1]. Mislove et al measure the structure of Flickr, YouTube, LiveJournal, and Orkut [22], and observe the growth of the Flickr social network [21]. Java et al study the topological and geographical properties of Twitter [13].…”

Section: Related Workmentioning

confidence: 99%

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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Growth of the flickr social network

Cited by 361 publications

References 8 publications

Measurement-calibrated graph models for social network experiments

Measurement-calibrated graph models for social network experiments

Assessing the Effects of a Soft Cut-Off in the Twitter Social Network

Understanding latent interactions in online social networks

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