  We addressed the problem of detecting the change in behavior of information
diffusion from a small amount of observation data, where the behavior changes
were assumed to be effectively reflected in changes in the diffusion parameter
value. The problem is to detect where in time and how long this change
persisted and how big this change is. We solved this problem by searching the
change pattern that maximizes the likelihood of generating the observed
diffusion sequences. The naive learning algorithm has to iteratively update the
patten boundaries, each requiring optimization of diffusion parameters by the
EM algorithm, and is very inefficient. We devised a very efficient search
algorithm using the derivative of likelihood which avoids parameter value
optimization during the search. The results tested using three real world
network structures confirmed that the algorithm can efficiently identify the
correct change pattern. We further compared our algorithm with the naive method
that finds the best combination of change boundaries by an exhaustive search
through a set of randomly selected boundary candidates, and showed that the
proposed algorithm far outperforms the native method both in terms of accuracy
and computation time.
