  Novelty attracts attention like popularity. Hence predicting novelty is as
important as popularity. Novelty is the side effect of competition and aging in
evolving systems. Recent behavior or recent link gain in networks plays an
important role in emergence or trend. We exploited this wisdom and came up with
two models considering different scenarios and systems. Where recent behavior
dominates over total behavior (total link gain) in the first one, and recent
behavior is as important as total behavior for future link gain in second one.
It suppose that random walker walks on a network and can jump to any node, the
probablity of jumping or making connection to other node is based on which node
is recently more active or receiving more links. In our assumption random
walker can also jump to node which is already popular but recently not popular.
We are able to predict rising novelties or popular nodes which is generally
suppressed under preferential attachment effect. To show performance of our
model we have conducted experiments on four real data sets namely, MovieLens,
Netflix, Facebook and Arxiv High Energy Physics paper citation. For testing our
model we used four information retrieval indices namely Precision, Novelty,
Area Under Receiving Operating Characteristic(AUC) and Kendal's rank
correlation coefficient. We have used four benchmark models for validating our
proposed models. Although our model doesn't perform better in all the cases
but, it has theoretical significance in working better for recent behavior
dominant systems.
