Characterizing user behavior in online social networks

Benevenuto, Fabrí­cio; Rodrigues, Tiago; Cha, Meeyoung; Almeida, Virgílio A. F.

doi:10.1145/1644893.1644900

Cited by 643 publications

(411 citation statements)

References 20 publications

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“…Active users either posted or sent at least one message and strictly active sent at least one of each. Note that active users are a small fraction of the total number of users in the network, as previously reported in the literature Benevenuto et al (2009).…”

Section: Evaluating the Feasibility Of Communication Inference In Onlsupporting

confidence: 65%

“…Benevenuto et al have proposed a model of OSN user behaviour that, to the best of our knowledge, is the most ambitious and comprehensive so far Benevenuto et al (2009Benevenuto et al ( , 2012. They provide, among other features, a characterisation of session timing, the frequency and type of activities performed in the OSN and the number of friends users interact with.…”

Section: Users' Activitymentioning

confidence: 99%

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Towards Inferring Communication Patterns in Online Social Networks

Balsa

Pérez-Solà

Díaz

2017

ACM Trans. Internet Technol.

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The separation between the public and private spheres on online social networks is known to be at best blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the OSN provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this paper, we study the relationship between private communication patterns and publicly available OSN data. Such relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key towards developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision.

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Section: Evaluating the Feasibility Of Communication Inference In Onlsupporting

confidence: 65%

Section: Users' Activitymentioning

confidence: 99%

Towards Inferring Communication Patterns in Online Social Networks

Balsa

Pérez-Solà

Díaz

2017

ACM Trans. Internet Technol.

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“…Existing literature studying social networks has shown that popular real social networks have the characteristics of a small-world network [12,15,5]. We consider the three most robust measures, i.e., the social degrees, the clustering coefficient, and the average path length of the network topology.…”

Section: Case Study: Generating Social Network Datamentioning

confidence: 99%

“…This suggests that S3G2 can generate extremely large graphs quickly on a Hadoop cluster with large resources. There is a lot of work studying the characteristics of social networks [11,7,12,15,5,1,9] and also on the generation of random graphs having global properties similar to a social network [14,3,4,10,6,8]. However, to the best of our knowledge, there is no generator that creates a synthetic social graph with correlations.…”

Section: Case Study: Generating Social Network Datamentioning

confidence: 99%

S3G2: A Scalable Structure-Correlated Social Graph Generator

Pham

Boncz

Erling

2013

Selected Topics in Performance Evaluation and Benchmarking

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Abstract. Benchmarking graph-oriented database workloads and graph-oriented database systems is increasingly becoming relevant in analytical Big Data tasks, such as social network analysis. In graph data, structure is not mainly found inside the nodes, but especially in the way nodes happen to be connected, i.e. structural correlations. Because such structural correlations determine join fan-outs experienced by graph analysis algorithms and graph query executors, they are an essential, yet typically neglected, ingredient of synthetic graph generators. To address this, we present S3G2: a Scalable Structure-correlated Social Graph Generator. This graph generator creates a synthetic social graph, containing non-uniform value distributions and structural correlations, which is intended as test data for scalable graph analysis algorithms and graph database systems. We generalize the problem to decompose correlated graph generation in multiple passes that each focus on one so-called correlation dimension; each of which can be mapped to a MapReduce task. We show that S3G2 can generate social graphs that (i) share well-known graph connectivity characteristics typically found in real social graphs (ii) contain certain plausible structural correlations that influence the performance of graph analysis algorithms and queries, and (iii) can be quickly generated at huge sizes on common cluster hardware.Data in real life is correlated; e.g. people living in Germany have a different distribution in names than people in Italy (location), and people who went to the same university in the same period have a much higher probability to be friends in a social network. Such correlations can strongly influence the intermediate result sizes of query plans, the effectiveness of indexing strategies, and cause absence or presence of locality in data access patterns. Regarding intermediate result sizes of selections, consider:SELECT personID FROM person WHERE firstName = 'Joachim' AND addressCountry = 'Germany' Query optimizers commonly use the independence assumption for estimating the result size of conjunctive predicates, by multiplying the estimates for the individual predicates. This would underestimate this result size, since Joachim is more common in Germany than in most other countries; similar would happen e.g. when querying for firstName 'Cesare' from 'Italy'. Overestimation can also easily happen, if we would query for 'Cesare' from 'Germany' or 'Joachim' from 'Italy ' (i.e. anti-correlation).This correlation problem has been recognized in relational database systems as relevant, and some work exists to detect correlated properties inside the same table (e.g., see [13]). Still, employing techniques for the detection of correlation is hardly mainstream in relational database management, and this is even more so when we start considering correlations between predicates that are separated by joins. Consider for instance the DBLP example of co-authorship of papers that counts the number of authors that have published both in TODS and...

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“…Benevenuto et al collect detailed click-stream data from a Brazilian social network aggregator, and measure silent activities like browsing [3]. Schneider et al extract click-streams from passively monitored network traffic and make similar measurements [25].…”

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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Characterizing user behavior in online social networks

Cited by 643 publications

References 20 publications

Towards Inferring Communication Patterns in Online Social Networks

Towards Inferring Communication Patterns in Online Social Networks

S3G2: A Scalable Structure-Correlated Social Graph Generator

Understanding latent interactions in online social networks

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