Name
Abstract | ||
|---|---|---|
Existing models of information diffusion assume that peer influence is the main reason for the observed propagation patterns. In this paper, we examine the role of authority pressure on the observed information cascades. We model this intuition by characterizing some nodes in the network as "authority" nodes. These are nodes that can influence large number of peers, while themselves cannot be influenced by peers. We propose a model that associates with every item two parameters that quantify the impact of the peer and the authority pressure on the item's propagation. Given a network and the observed diffusion patterns of the item, we learn these parameters from the data and characterize the item as peer- or authority-propagated. We also develop a randomization test that evaluates the statistical significance of our findings and makes our item characterization robust to noise. Our experiments with real data from online media and scientific-collaboration networks indicate that there is a strong signal of authority pressure in these networks. |
Year | Venue | Keywords |
|---|---|---|
2011 | ECML/PKDD (1) | observed information cascade,scientific-collaboration network,authority pressure,large number,observed propagation pattern,online media,main reason,item characterization,information-propagation model,observed diffusion pattern |
Field | DocType | Volume |
Data mining,Peer influence,Computer science,Information cascade,Intuition,Information propagation,Resampling,Digital media | Conference | 6911 |
ISSN | Citations | PageRank |
0302-9743 | 7 | 0.51 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aris Anagnostopoulos | 1 | 1054 | 67.08 |
| George Brova | 2 | 18 | 1.14 |
| Evimaria Terzi | 3 | 1580 | 83.54 |