Research into Structure of Two-Way Network Community
Abstract
Two-way networks are a special type of graph consisting of two different types of nodes. The edges between nodes exist only between nodes of different types. Community partitioning is an important link in network analysis, which involves dividing network nodes into several clusters or communities in order to better understand and identify the structural and functional characteristics of the network. For two-way networks, community partitioning can help to understand how close relationships are formed between different types of nodes. The structure of two-way network is used in many fields. Due to its unique structural characteristics, traditional community partitioning methods often cannot be directly applied to two-way networks, which makes the proposed study of community partitioning of two-way networks more meaningful.
The main objective of this research is to develop a new community partitioning algorithm based on modularity increment. This algorithm aims to overcome the limitations of existing BRIM methods and improve the accuracy and efficiency of community partitioning in two-way networks. Modularity is an important indicator of the quality of network partitioning, and the degree of modularity takes a value from -1 to 1. When the value is positive, the community structure is more dense than the random network, and when the value is negative, the community structure is less dense than the random network.
The problems addressed in this work were: first, how to determine and calculate the modularity increment in two-way networks; second, how to develop an efficient algorithm to maximise the modularity increment to achieve the best community partitioning effect; finally, how to test the effectiveness and superiority of the new algorithm in community partitioning of two-way networks.
The research results show that the new algorithm performs well in separating two-way network communities, improves the accuracy and efficiency of community separation, and can identify the structural characteristics of the network more effectively, thus providing a powerful analysis tool in related fields.
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