Dynamic ranking objects based on time varying event network

The traditional node-ranking algorithms just rank the nodes of one network by the aggregation function to the network and do not refer to the sequence of the network events. We propose TVNM with dynamic ranking objects, which is a framework for quantifying the relationship between objects over time. And we also propose the modified PageRank algorithm based on TVNM. We compare the PageRank algorithm to the modified PageRank algorithm on a large organizational data set.