Graphs and Networks
Graphs and networks theory
... these networks are determined by this ...the networks representing a real-world system we need an awful amount of information, such as: number of nodes and links, degree distribution, degree-degree ...
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Randomized algorithms and upper bounds for multiple domination in graphs and networks
... Domination is one of the fundamental concepts in graph theory with var- ious applications to wireless and ad hoc networks, biological networks, dis- tributed computing, social networks and web ...
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Deep Learning on Graphs using Graph Convolutional Networks
... social networks which have become an integral part of our life and have remarkably changed the way humans interact with each other and the ...social networks contain a plethora of valuable information about ...
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From Random Graphs to Complex Networks:
... complex networks are, ...random graphs, the Erd˝os-R´eyni ...complex networks as we understand them at the time into ...complex networks, as well as some newer ...
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Complexity in Infinite Games on Graphs and Temporal Constraint Networks
... Temporal Networks (STNs) [44], i.e., directed weighted graphs where nodes represent events to be scheduled in time and arcs repre- sent temporal distance constraints between pairs of ...Temporal ...
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Kronecker Graphs: An Approach to Modeling Networks
... generate networks with such ...generate graphs for extrapolations, hypothesis testing, “what-if” scenarios, and simulations, when real graphs are difficult or impossible to ...
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Deep depth-based representations of graphs through deep learning networks
... all graphs, we propose to use the prototype representations to train a deep autoencoder network, that is opti- mized using Stochastic Gradient Descent together with the Deep Belief Network for ...all graphs ...
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Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks
... knowledge graphs, in our rush to explore ever sophisticated deep learning tech- niques, we have not adequately examined simple, strong baselines in a rigorous ...neural networks, even though it is easy to ...
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Determining Vision Graphs for Distributed Camera Networks Using Feature Digests
... sensor networks have made feasible a distributed camera network, in which cameras and processing nodes may be spread over a wide geographical area, with no central- ized processor and limited ability to ...
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Double-simulated annealing model for mapping of graphs to single-row networks
... Connected graph is a graph where every pair of distinct vertices in the graph is connected either directly or indirectly. In our real life, many science and engineering applications can be reduced into the connected ...
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SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks
... SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve such a goal, SeMi builds the semantic models ...
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A distributed approach to precoder selection using factor graphs for wireless communication networks
... and turbo codes, generalized Kalman filtering, fast Fourier transform, etc. One classic example motivated the most: the prisoner’s dilemma [6]. In a few words, the two pris- oners find an equilibrium point if both greedily ...
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The Visualization of Cattle Movement Data in The State of Pará in 2016 Through Networks of Animal Transit Graphs and Guides
... of Networks are methodological concepts that describe the interactions between individuals in a group, as well as the collective behavior of a group ...of Networks have only recently been added to the ...
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The Dynamic to Static Conversion of Dynamic Fault Trees Using Stochastic Dependency Graphs and Stochastic Activity Networks
... Medium level models are used to represent the behavior and the fault logic of the system. The behavioral model is built using the knowledge contained in the DG model. A special class of GSPN—Stochastic Activity ...
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Wirelength of Circulant Networks into Wheel Related Graphs
... Circulant graphs are an important class of topological structures of interconnection networks which have been used for decades in the design of computer and telecommunication networks due to their ...
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Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs
... For graphs defined over continuous variables, the DSP algorithm can be imple- mented through two sets of messages in order to compute the higher-order derivatives of the joint ...in graphs with cycles: an ...
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On Eccentric Graphs of Broom Graphs
... Eccentric graphs of other special kinds of graphs such as intersection graphs [10], trapezoid graphs [2] and so on can be ...eccentric graphs of the type considered here in graph based ...
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Some Domination Parameters of Direct Product Graphs of Cayley Graphs with Arithmetic Graphs
... in graphs has been an extensively research branch of graph ...and networks design, mobile computing, resource allocation and ...Cayley graphs are excellent models for interconnection networks, ...
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A Study on Spectrum of Regular Graphs and Line Graphs
... Configurations of vertices and connection occur in a great diversity of applications. They may represent physical networks, such as electrical circuits, roadways or organic molecules. Any they are used in ...
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PLANAR GRAPHS, BIPLANAR GRAPHS AND GRAPH THICKNESS
... Within the branch of combinatorics exists a field of mathematics called graph theory. Graphs can be used to model many everyday circumstances, such as transporta- tion networks and electrical circuits. ...
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