Open AccessIdentifying important nodes from content-associated heterogeneous graph by LeaderRank
Author(s) -
Yulong Dai,
Qiyou Shen,
Xiangqian Xu,
Jun Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2113/1/012082
Subject(s) - computer science , homogeneous , theoretical computer science , graph , ranking (information retrieval) , node (physics) , set (abstract data type) , type (biology) , content (measure theory) , data mining , information retrieval , mathematics , combinatorics , ecology , mathematical analysis , structural engineering , biology , engineering , programming language
Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.