Information networks are becoming increasingly important in practice. However, their escalating complexity is gradually impeding the efficiency of data mining. A novel network schema called the Behavior Schema of Info...Information networks are becoming increasingly important in practice. However, their escalating complexity is gradually impeding the efficiency of data mining. A novel network schema called the Behavior Schema of Information Networks (BSIN) is proposed to address this issue. This work defines the behavior of nodes as connected paths in BSIN, proposes a novel function distinguish behavior differences, and introduces approximate bisimulation into the acquisition of quotient sets for node types. The major highlight of BSIN is its ability to directly obtain a high-efficiency network on the basis of approximate bisimulation, rather than reducing the existing information network. It provides an effective representation of information networks, and the resulting novel network has a simple structure that more efficiently expresses semantic information than current network representations. The theoretical analysis of the connected paths between the original and the obtained networks demonstrates that errors are controllable;and semantic information is approximately retained. Case studies show that BSIN yields a simple network and is highly cost-effective.展开更多
基金supported by the National Natural Science Foundation of China(No.12261027)the Innovation Project of Guangxi Graduate Education(No.YCBZ2021027).
文摘Information networks are becoming increasingly important in practice. However, their escalating complexity is gradually impeding the efficiency of data mining. A novel network schema called the Behavior Schema of Information Networks (BSIN) is proposed to address this issue. This work defines the behavior of nodes as connected paths in BSIN, proposes a novel function distinguish behavior differences, and introduces approximate bisimulation into the acquisition of quotient sets for node types. The major highlight of BSIN is its ability to directly obtain a high-efficiency network on the basis of approximate bisimulation, rather than reducing the existing information network. It provides an effective representation of information networks, and the resulting novel network has a simple structure that more efficiently expresses semantic information than current network representations. The theoretical analysis of the connected paths between the original and the obtained networks demonstrates that errors are controllable;and semantic information is approximately retained. Case studies show that BSIN yields a simple network and is highly cost-effective.
