Name
Title | ||
|---|---|---|
Persistent Cascades: Measuring Fundamental Communication Structure In Social Networks |
Abstract | ||
|---|---|---|
We define a new structural property of large-scale communication networks consisting of the persistent patterns of communication among users. We term these patterns "persistent cascades,"and claim they represent a strong estimate of actual information spread. Using metrics of inexact tree matching, we group these cascades into classes which we then argue represent the communication structure of a local network. This differs from existing work in that (1) we are focused on recurring patterns among specific users, not abstract motifs (e.g. the prevalence of triangles or other structures in the graph, regardless of user), and (2) we allow for inexact matching (not necessarily isomorphic graphs) to better account for the noisiness of human communication patterns. We find that analysis of these classes of cascades reveals new insights about information spread and the influence of certain users, based on three large mobile phone record datasets. For example, we find distinct groups of weekend vs. workweek spreaders not evident in the standard aggregated network. Finally, we create the communication network induced by these persistent structures, and we show the effect this has on measurements of centrality. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Communication,Communication network,Communication structure |
Field | DocType | Citations |
Data mining,Telecommunications network,Social network,Graph isomorphism,Computer science,Centrality,Local area network,Artificial intelligence,Mobile phone,Human communication,Big data,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.38 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Morse | 1 | 2 | 0.38 |
| Marta C. González | 2 | 299 | 18.26 |
| Natasha Markuzon | 3 | 3 | 3.12 |