Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli- cable to a heterogeneous network. Registered patent history data denotes the innovations and the achieve...Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli- cable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor＇s interesting topics using a latent Dirichlet allocation （LDA） model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.展开更多
基金Project supported by the National Science and Technology Support Plan （No. 2013BAH21B02-01）, Beijing Natural Science Foundation （No. 4153058）, and Shanghai Key Laboratory of Intelligent Information Processing （No. IIPL-2014-004）
文摘Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli- cable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor＇s interesting topics using a latent Dirichlet allocation （LDA） model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.
