Information cartography: creating zoomable, large-scale maps of information
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper, we present a general framework to quantify changes in temporally evolving data. We focus on changes that materialize due to evolution and interactions of features extracted from the data. The changes are captured by the following key transformations: create, merge, split, continue, and cease. First, we identify various factors which influence the importance of each transformation. These factors are then combined using a weight vector. The weight vector encapsulates domain knowledge. We evaluate our algorithm using the following datasets: DBLP, IMDB, Text and Scientific Dataset.