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Are Graph Convolutional Networks With Random Weights Feasible?
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks, are receiving extensive attention for their powerful capability in learning node representations on graphs. There are various extensions, either in sampling and/or node
Changqin Huang +5 more
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ACM Computing Surveys, 2019
Random graph (RG) models play a central role in complex networks analysis. They help us to understand, control, and predict phenomena occurring, for instance, in social networks, biological networks, the Internet, and so on.
M. Drobyshevskiy, D. Turdakov
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Random graph (RG) models play a central role in complex networks analysis. They help us to understand, control, and predict phenomena occurring, for instance, in social networks, biological networks, the Internet, and so on.
M. Drobyshevskiy, D. Turdakov
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RGDAN: A random graph diffusion attention network for traffic prediction
Neural NetworksTraffic Prediction based on graph structures is a challenging task given that road networks are typically complex structures and the data to be analyzed contains variable temporal features. Further, the quality of the spatial feature extraction is highly
Jinghua Fan +5 more
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The Condensation Phase Transition in Random Graph Coloring
International Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2014Based on a non-rigorous formalism called the “cavity method”, physicists have put forward intriguing predictions on phase transitions in diluted mean-field models, in which the geometry of interactions is induced by a sparse random graph or hypergraph ...
V. Bapst +4 more
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Random Graphs and Graph Optimization Problems
SIAM Journal on Computing, 1980One major difficulty in analyzing algorithms for graph optimization problems is that the probabilistic behavior of the optimum solutions to most of the important problems is generally unknown. We present a general method for relating some well-known results regarding the probability of existence of certain subgraphs in random graphs to the ...
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1992
Abstract In this chapter, we indicate how the general results of Chapter 2 can be used in problems involving random graphs. In Section 5.1 we consider the number of copies of a small graph G contained in Kn,p. Three cases are distinguished, counting all copies of G, induced copies of G and isolated copies of G, illustrating different ...
A D Barbour, Lars Holst, Svante Janson
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Abstract In this chapter, we indicate how the general results of Chapter 2 can be used in problems involving random graphs. In Section 5.1 we consider the number of copies of a small graph G contained in Kn,p. Three cases are distinguished, counting all copies of G, induced copies of G and isolated copies of G, illustrating different ...
A D Barbour, Lars Holst, Svante Janson
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2010
Abstract An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient.
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Abstract An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient.
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2001
In this second edition of the now classic text, the already extensive treatment given in the first edition has been heavily revised by the author. The addition of two new sections, numerous new results and 150 references means that this represents a comprehensive account of random graph theory. The theory (founded by Erdös and Rényi in the late fifties)
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In this second edition of the now classic text, the already extensive treatment given in the first edition has been heavily revised by the author. The addition of two new sections, numerous new results and 150 references means that this represents a comprehensive account of random graph theory. The theory (founded by Erdös and Rényi in the late fifties)
openaire +1 more source
Random Graphs, Random Triangle-Free Graphs, and Random Partial Orders
2001While everybody seems to immediately understand and accept the commonly used model of a random graph - simply toss a coin for every edge to decide whether it is there - the situation gets harder when we require that the random graph must satisfy some additional constraints such as having no triangles or being transitive.
Hans Jürgen Prömel, Anusch Taraz
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