We also have find a nonextensive threshold value in the nonextensive local structure entropy. When the value of $q$ is bigger than the nonextensive threshold value, change the value of $q$ will has no influence on the property of the local structure entropy, and different complex networks ...
In the research of complex networks, structural analysis can be explained as finding the information hidden in the network’s topological structure. Thus, the way and the range of the structural inf...
Traditional models of human brain activity often represent it as a network of pairwise interactions between brain regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order interactions from temporal brain signals i
The trends to equilibrium are determined in terms of the Boltzmann H-functional H[f](t)=∫R2dflogfdvdx. In particular, starting from an initial distribution with finite entropy, f(x,v,t) converges in relative entropy to the equilibrium solution exponentially fast. 2.2 Vlasov-Fokker-...
The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information
In this paper, we propose a new method, called Global and Local Structure(GLS), for identifying influential nodes in complex networks. It considers both the local structure and the global structure of the network. The information sharing ability of a node with all other nodes and the influence...
Once these bounds are selected, we choose the maximum entropy prior distribution subject to these bounds, which is the least informative prior on R E and ensures that we do not unnecessarily bias the result. This distribution is $$\pi(E,\theta) = \frac{1}{s \cdot \text{Vol}(R_{E...
Meanwhile, the average shortest path theory, redefined with a semi-local structure, addresses the issue of identifying influential nodes in large-scale networks. The Susceptible-Infected (SI) model and Kendall's correlation coefficient are used to evaluate the performance of our centrality measure. ...
The structure of the model is shown in Fig. 2. First, the original input image undergoes multiple convolutions, BatchNorm, and activation to reduce the feature resolution and increase the feature dimensionality. Then it goes through a graph convolution module ViG27 for feature convergence. Next ...
priori information of communities, our local clustering algorithm yields promising results that aid our understanding of the organization and structure in these massive complex networks. Preliminaries Let\(G=(V,E)\)be an undirected simple graph onnnodes, whereVis the set of nodes andEis the set ...