Understanding latent interactions in online social networks

Abstract

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.

Assessing the Evolution of Google+ in Its First Two Years

“…Previous works have separately studied the evolution of the relative size of the network elements for specific OSNs (Flickr and Yahoo 360) [41], the growth of an OSN, and the evolution of its graph properties [24], [32], [33], [44], [46], [49], [57] or the evolution of the interactions between users [39], [54] and the user availability [28]. In this paper, instead of looking at a specific aspect, we perform a comprehensive analysis to study the evolution of different key aspects of G+, namely the system growth, the representative of the different network elements, the LCC connectivity and activity properties, and the level of information sharing.…”

Optimizing Inter-server Communication for Online Social Networks

“…Two earlier studies [16], [21] used clickstreams to analyze how users interact in OSNs and observed that 92% of user activities on OSNs are profile browsing, which implies that the majority of user interactions are latent interactions. Jiang et al [22] performed a detailed measurement and constructed latent interaction graphs. Similar to other work [10], we use the features reported by these empirical studies to generate user interactions for our simulations.…”

“…Specifically, the sets of read rates and write rates for all users are first generated from the power-law distribution with an exponent of 3.5 [22]. Following the statistics reported in [16], the ratio between the total read rate and total write rate is set at 0.92/0.08.…”

“…Then, each user is assigned a read rate and a write rate from the above sets. In this process, we control the Spearman's rank correlation coefficient [23] between the read/write rate of each user and her social degree (the number of her neighbors in the social graph), which is set to 0.7 [22]. The write rate assigned to each user represents the rate at which the user updates her data.…”

Les réseaux sociaux numériques en Chine : une constellation de petits mondes

“…du réseau Renren, le second réseau social numérique du pays, ainsi qu'aux historiques détaillés des visites de 61 000 utilisateurs du réseau Renren de l'Université de Pékin enregistrés sur 90 jours (Jing, Wilson et al, 2010). L'objectif était de mesurer la popularité relative de certains profils, la réciprocité des visites de profils et l'impact des mises à jour sur la popularité de certaines identités.…”