A tutorial on graph models for scheduling round-robin sports tournaments

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In this paper we study first passage percolation on a random graph model, the configuration model. We first introduce the notions of weighted diameter, which is the maximum of the weighted lengths of all optimal paths between any two vertices in the graph, ...

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying ...

A graph is a versatile data structure facilitating representation of interactions among objects in various complex systems. Very often these objects have attributes whose measurements change over time, reflecting the dynamics of the system. This general da ...

We study in this thesis the asymptotic behavior of optimal paths on a random graph model, the configuration model, for which we assign continuous random positive weights on its edges. We start by describing the asymptotic behavior of the diameter and the f ...

We propose the Recursive Non-autoregressive Graph-to-graph Transformer architecture (RNG-Tr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dep ...

Graph processing systems are used in a wide variety of fields, ranging from biology to social networks. Algorithms to mine graphs incur many random accesses, and the sparse nature of the graphs of interest, exacerbates this. As DRAM sustains high bandwidt ...

Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...

We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an ...

Non-invasive characterization of brain structure has been made possible by the introduction of magnetic resonance imaging (MRI). Graph modeling of structural connectivity has been useful, but is often limited to defining nodes as regions from a brain atlas ...

Let F be a fixed graph. The rainbow Turan number of F is defined as the maximum number of edges in a graph on n vertices that has a proper edge-coloring with no rainbow copy of F (i.e., a copy of F all of whose edges have different colours). The systematic ...

A tutorial on graph models for scheduling round-robin sports tournaments

Chat with Graph Search

Flooding And Diameter In General Weighted Random Graphs

Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

Dynamic pattern recognition in large-scale graphs with applications to social networks

Non-Exponential Variations for Classical Results in First Passage Percolation

Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement

Efficient large-scale graph processing: optimisations for storage, performance and evolving graphs

Slepian Guided Filtering of Graph Signals

Graph Learning with Partial Observations: Role of Degree Concentration

GOT: An Optimal Transport framework for Graph comparison

Graph Spectral Analysis Of Voxel-Wise Brain Graphs From Diffusion-Weighted Mri

On the Rainbow Turan number of paths