Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Understanding online social network usage from a network perspective
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Evolution of an online social aggregation network: an empirical study
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Understanding latent interactions in online social networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Measuring user behavior in online social networks
IEEE Network: The Magazine of Global Internetworking
Sizing up online social networks
IEEE Network: The Magazine of Global Internetworking
On the impact of users availability in OSNs
Proceedings of the Fifth Workshop on Social Network Systems
Talking in circles: selective sharing in google+
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling user posting behavior on social media
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Look who I found: understanding the effects of sharing curated friend groups
Proceedings of the 3rd Annual ACM Web Science Conference
Tracing the birth of an OSN: social graph and profile analysis in Google+
Proceedings of the 3rd Annual ACM Web Science Conference
On the bursty evolution of online social networks
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
Evolution of social-attribute networks: measurements, modeling, and implications using google+
Proceedings of the 2012 ACM conference on Internet measurement conference
New kid on the block: exploring the google+ social graph
Proceedings of the 2012 ACM conference on Internet measurement conference
Multi-scale dynamics in a massive online social network
Proceedings of the 2012 ACM conference on Internet measurement conference
Video telephony for end-consumers: measurement study of Google+, iChat, and Skype
Proceedings of the 2012 ACM conference on Internet measurement conference
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In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?". This paper tackles the above question by presenting a detailed characterization of G+ based on large scale measurements. We identify the main components of G+ structure, characterize the key features of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics with other major OSNs. We show that despite the dramatic growth in the size of G+, the relative size of LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity. To our knowledge, our study offers the most comprehensive characterization of G+ based on the largest collected data sets.