Collecting the internet AS-level topology
ACM SIGCOMM Computer Communication Review
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
Quasi-random graphs with given degree sequences
Random Structures & Algorithms
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Social Synchrony: Predicting Mimicry of User Actions in Online Social Media
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Weighted spectral distribution for internet topology analysis: theory and applications
IEEE/ACM Transactions on Networking (TON)
Network growth and the spectral evolution model
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Diversity is an important characterization aspect for online social networks that usually denotes the homogeneity of a network's content and structure. This paper addresses the fundamental question of diversity evolution in large-scale online communities over time. In doing so, we study different established notions of network diversity, based on paths in the network, degree distributions, eigenvalues, cycle distributions, and control models. This leads to five appropriate characteristic network statistics that capture corresponding aspects of network diversity: effective diameter, Gini coefficient, fractional network rank, weighted spectral distribution, and number of driver nodes of a network. Consequently, we present and discuss comprehensive experiments with a broad range of directed, undirected, and bipartite networks from several different network categories -- including hyperlink, interaction, and social networks. An important general observation is that network diversity shrinks over time. From the conceptual perspective, our work generalizes previous work on shrinking network diameters, putting it in the context of network diversity. We explain our observations by means of established network models and introduce the novel notion of eigenvalue centrality preferential attachment.