Rising Novelties on Evolving Networks: Recent Behavior Dominant and Non-Dominant Model
This work addresses the challenge of predicting novelty in dynamic systems like social networks, but it is incremental as it builds on existing network models with a focus on recent behavior.
The paper tackles the problem of predicting rising novelties (newly popular nodes) in evolving networks by proposing two models that incorporate recent behavior, such as recent link gains, to overcome limitations of preferential attachment. It shows performance on real datasets like MovieLens and Facebook, with results validated using metrics like Precision and AUC, though it does not outperform all benchmarks in every case.
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.