IVIS: Search and visualization on heterogeneous information networks

摘要

We build a system to support search and visualization on heterogeneous information networks.We first build our system on a specialized heterogeneous information network: DBLP. The systemaims to facilitate people, especially computer science researchers, toward a better understandingand user experience about academic information networks. Then we extend our system to theWeb. Our results are much more intuitive and knowledgeable than the simple top-k blue links fromtraditional search engines, and bring more meaningful structural results with correlated entities.We also investigate the ranking algorithm, and we show that the personalized PageRank andproposed Hetero-personalized PageRank outperform the TF-IDF ranking or mixture of TF-IDFand authority ranking. Our work opens several directions for future research.