Many online social networks such as Twitter, Google+, Flickr and Youtube are directed in nature, and have been shown to exhibit a nontrivial amount of reciprocity. Reciprocity is defined as the ratio of the number of reciprocal edges to the total number of edges in the network, and has been well studied in the literature. However, little attention is given to understand the connectivity or network form by the reciprocal edges themselves (reciprocal network), its structural properties, and how it evolves over time. In this paper, we bridge this gap by presenting a comprehensive measurement-based characterization of the connectivity among reciprocal edges in Google+ and their evolution over time, with the goal to gain insight into the structural properties of the reciprocal network. Our analysis shows that the reciprocal network of Google+ reveals some important user behavior patterns, which reflect how the social network was being adopted over time.
|Original language||English (US)|
|Title of host publication||Computational Social Networks - 5th International Conference, CSoNet 2016, Proceedings|
|Editors||Hien T. Nguyen, Vaclav Snasel|
|Number of pages||3|
|State||Published - 2016|
|Event||5th International Conference on Computational Social Networks, CSoNet 2016 - Ho Chi Minh City, Viet Nam|
Duration: Aug 2 2016 → Aug 4 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||5th International Conference on Computational Social Networks, CSoNet 2016|
|City||Ho Chi Minh City|
|Period||8/2/16 → 8/4/16|
Bibliographical noteFunding Information:
This research was supported in part by a Raytheon/NSF subcontract 9500012169/CNS-1346688, DTRA grants HDTRA1- 09-1-0050 and HDTRA1-14-1-0040, DoD ARO MURI Award W911NF-12-1-0385, and NSF grants CNS-1117536, CRI-1305237 and CNS-1411636. We thank the authors of  for the datasets and the workshop reviewers for helpful comments.
© Springer International Publishing Switzerland 2016.
- Reciprocal network