Results 31 to 40 of about 942,096 (280)
On Topological Properties of Degree-Based Entropy of Hex-Derived Network of Type 3
Journal of Function Spaces, 2022 Hex-derived network has an assortment of significant applications in medicine store, equipment, and network organization. Graph entropy depends upon distribution probability of vertex set and on graph itself.
Cun-Bin Zhu +4 more
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Entropy and Multi-Fractal Analysis in Complex Fractal Systems Using Graph Theory
Axioms, 2023 In 1997, Sierpinski graphs, S(n,k), were obtained by Klavzar and Milutinovic. The graph S(1,k) represents the complete graph Kk and S(n,3) is known as the graph of the Tower of Hanoi. Through generalizing the notion of a Sierpinski graph, a graph named a
Zeeshan Saleem Mufti +3 more
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The entropy of a thermodynamic graph
CoRR, 2016 We introduce an algorithmic model of heat conduction, the thermodynamic graph. The thermodynamic graph is analogous to meshes in the finite difference method in the sense that the calculation of temperature is carried out at the vertices of the graph, and the edges indicate the direct heat exchange between the vertices.
O. Kurganskyy, A. J. Maksimova
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The Structure Entropy-Based Node Importance Ranking Method for Graph Data
Entropy, 2023 Due to its wide application across many disciplines, how to make an efficient ranking for nodes in graph data has become an urgent topic. It is well-known that most classical methods only consider the local structure information of nodes, but ignore the ...
Shihu Liu, Haiyan Gao
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Minimum entropy principle guided graph neural networks
, 2022 Graph Neural Networks (GNNs) have become the mainstream way of learning graphstructured data consisting of nodes and edges. Generally, GNNs encode nodes and graphs to lower-dimensional vectorized representations for serving node-level and graphlevel ...
Zhenyu Yang (12287519)
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Minimum Entropy Principle Guided Graph Neural Networks
, 2023 Graph neural networks (GNNs) are now the mainstream method for mining graph-structured data and learning low-dimensional node- and graph-level embeddings to serve downstream tasks.
Jia Wu (169990) +8 more
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Demystification of Graph and Information Entropy [PDF]
, 2020 Shannon entropy is an information-theoretic measure of unpredictability in probabilistic models. Recently, it has been used to form a tool, called the von Neumann entropy, to study quantum mechanics and network flows by appealing to algebraic properties ...
Frederickson, Bryce
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On Computation of Entropy of Hex-Derived Network
Complexity, 2021 A graph’s entropy is a functional one, based on both the graph itself and the distribution of probability on its vertex set. In the theory of information, graph entropy has its origins.
Pingping Song +4 more
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Detecting Depression Using Single-Channel EEG and Graph Methods
Mathematics, 2022 Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals.
Guohun Zhu +7 more
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Generalizing Körner’s graph entropy to graphons
European Journal of Combinatorics, 2023 Körner introduced the notion of graph entropy in 1973 as the minimal code rate of a natural coding problem where not all pairs of letters can be distinguished in the alphabet. Later it turned out that it can be expressed as the solution of a minimization problem over the so-called vertex-packing polytope.
Harangi, Viktor, Niu, Xueyan, Bai, Bo
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