  Social networks are the natural space for the spreading of information and
influence and have become a media themselves. Several models capturing that
diffusion process have been proposed, most of them based on the Independent
Cascade (IC) model or on the Linear Threshold (LT) model. The IC model is
probabilistic while the LT model relies on the knowledge of an actor to be
convinced, reflected in an associated individual threshold.
  Although the LT-based models contemplate an individual threshold for each
actor in the network, the existing studies so far have always considered a
threshold of 0.5 equal in all actors (i.e., a simple majority activation
criterion).
  Our main objective in this work is to start the study on how the
dissemination of information on networks behaves when we consider other options
for setting those thresholds and how many network actors end up being
influenced by this dissemination. For doing so, we consider a recently
introduced centrality measure based on the LT model, the Forward Linear
Threshold Rank (FLTR). We analyze experimentally the ranking properties for
several networks in which the influence resistance threshold follows different
schemes. Here we consider three different schemes: (1) uniform, in which all
players have the same value; (2) random, where each player is assigned a
threshold u.a.r. in a prescribed interval; and (3) determined by the value of
another centrality measure on the actor. Our results show that the selection
has a clear impact on the ranking, even quite significant and abrupt in some
cases. We conclude that the social networks ranks that provide the best
assignments for the individual thresholds are PageRank and FLTR.
