Abstract
Node similarity is a significant basis for analyzing features in complex network. For complex network with directed weighted edge, the complexity of the relationship among nodes and the diversity of relationship weights make measure node similarity complicated from the huge amounts of nodes. Therefore, a novel node similarity measure is proposed based on the design of node nearest neighbor local network relative weighted entropy. Firstly, degree and strength based directed weighted complex network model is constructed, on the basis of that, the Node Nearest Neighbor Local network is defined. The structural features of each node in the local network are quantized into a set of decision probabilities with multiple indicators. Furthermore, Node Connection Tightness is given to compute the influence of structural complex relationship in local network on node similarity. And then, to evaluate how the outward & inward degree and strength of nodes in the local network affect node similarity, the concept of Node Nearest Neighbor Local Network Relative Weighted Entropy is designed to define Node Relative Difference for measuring the structural difference between any two nodes. Accordingly, a novel node similarity measure is designed to measure the similarity between any pair of nodes in directed weighted complex network, and then the Similar Node Mining Algorithm is proposed to obtain most similar nodes. To clarify the availability and effectiveness of the proposed measure, two sets of experiments were conducted on real-world complex networks. The results show that the measure can not only mine nodes with the most similarity in the same module, but also mine the most similarity nodes from different modules.
Highlights
The emergence of complex systems has caused widespread concern in many areas
By utilizing the node relative difference between vi and vj, a novel Node Similarity Measure based on Node Nearest Neighbor Local Network Relative Weighted Entropy (LRWE-SNM) can be designed and expressed as follows
EXPERIMENTAL ANALYSIS of experiment, to verify the feasibility and rationality of Node Nearest Neighbor Local Network Relative Weighted Entropy based Similar Node Mining Algorithm, the algorithm is applied to two groups of reality complex networks with varying sizes. (i) E.coli transcription network [22] with 95 nodes and 213 edges
Summary
The emergence of complex systems has caused widespread concern in many areas. Most complex systems can be abstracted into a complex network of nodes and connected edges. In the study of complex network, one of the most important directions is node similarity measure, and measuring node similarity is a fundamental work. It is faster and simpler to measure, much more node information and complex relationships between nodes are lost, resulting in incomplete decision information of a large number of nodes This makes the dipartite degree of most node similarity obtained by measures is small, and node similarity cannot be accurately computed. Even if the weights are considered, the influence of nodes’ complex relationship information on node similarity in the local network topology cannot be effectively measured. Thence, in this paper, in order to measure node similarity in complex networks with directed weighted edges accurately, a degree and strength based directed weighted complex network model is constructed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
