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It is a correction paper on "P.J. Wan and C.W. Yi, "Coverage by Randomly Deployed Wireless Sensor Networks", IEEE Transaction On Information Theory, vol.52, No.6, June 2006." In the above paper, Lemma (4), on page 2659 play the key role for…

Information Theory · Computer Science 2009-04-23 Bhupendra Gupta

When facing graph signal processing tasks, the workhorse assumption is that the graph describing the support of the signals is known. However, in many relevant applications the available graph suffers from observation errors and…

Signal Processing · Electrical Eng. & Systems 2024-12-03 Samuel Rey , Victor M. Tenorio , Antonio G. Marques

Proofs are traditionally syntactic, inductively generated objects. This paper reformulates first-order logic (predicate calculus) with proofs which are graph-theoretic rather than syntactic. It defines a combinatorial proof of a formula…

Logic · Mathematics 2019-06-27 Dominic J. D. Hughes

Graphons $W$ can be used as stochastic models to sample graphs $G_n$ on $n$ nodes for $n$ arbitrarily large. A graphon $W$ is said to have the $H$-property if $G_n$ admits a decomposition into disjoint cycles with probability one as $n$…

Optimization and Control · Mathematics 2021-11-12 Mohamed-Ali Belabbas , Xudong Chen , Tamer Basar

Where graphs are used for modelling and specifying systems, consistency is an important concern. To be a valid model of a system, the graph structure must satisfy a number of constraints. To date, consistency has primarily been viewed as a…

Logic in Computer Science · Computer Science 2021-11-02 Jens Kosiol , Daniel Strüber , Gabriele Taentzer , Steffen Zschaler

A graphon satisfies the $H$-property if graphs sampled from it contain a Hamiltonian decomposition almost surely, which in turn implies that the corresponding network topologies are, e.g., structurally stable and structurally ensemble…

Optimization and Control · Mathematics 2024-02-16 Mohamed-Ali Belabbas , Xudong Chen

In a recent paper, Caron and Fox suggest a probabilistic model for sparse graphs which are exchangeable when associating each vertex with a time parameter in $\mathbb{R}_+$. Here we show that by generalizing the classical definition of…

Probability · Mathematics 2018-06-21 Christian Borgs , Jennifer T. Chayes , Henry Cohn , Nina Holden

We target the problem of provably computing the equivalence between two complex expression trees. To this end, we formalize the problem of equivalence between two such programs as finding a set of semantics-preserving rewrite rules from one…

Programming Languages · Computer Science 2021-06-10 Steve Kommrusch , Théo Barollet , Louis-Noël Pouchet

In the paper Compositionality of Component Fault Trees, we present a discussion of the compositionality of correctness of component fault trees. In this technical report, we present the formal proof of the central theorem of the…

Software Engineering · Computer Science 2019-07-24 Simon Greiner , Peter Munk , Arne Nordmann

Graph Neural Networks (GNNs) have achieved much success on graph-structured data. In light of this, there have been increasing interests in studying their expressive power. One line of work studies the capability of GNNs to approximate…

Machine Learning · Computer Science 2023-02-14 Zhengdao Chen , Soledad Villar , Lei Chen , Joan Bruna

The Gowers U^3 norm is one of a sequence of norms used in the study of arithmetic progressions. If G is an abelian group and A is a subset of G then the U^3(G) of the characteristic function 1_A is useful in the study of progressions of…

Number Theory · Mathematics 2023-12-08 Ben Green , Terence Tao

A graph's spectral wavelet signature determines a filtration, and consequently an associated set of extended persistence diagrams. We propose a framework that optimises the choice of wavelet for a dataset of graphs, such that their…

Signal Processing · Electrical Eng. & Systems 2023-06-28 Ka Man Yim , Jacob Leygonie

Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph…

Data Structures and Algorithms · Computer Science 2017-06-07 Bastien Pasdeloup , Vincent Gripon , Grégoire Mercier , Dominique Pastor , Michael G. Rabbat

This paper focuses on spectral graph convolutional neural networks (ConvNets), where filters are defined as elementwise multiplication in the frequency domain of a graph. In machine learning settings where the dataset consists of signals…

Machine Learning · Computer Science 2021-06-15 Ron Levie , Wei Huang , Lorenzo Bucci , Michael M. Bronstein , Gitta Kutyniok

We study the design of graph filters to implement arbitrary linear transformations between graph signals. Graph filters can be represented by matrix polynomials of the graph-shift operator, which captures the structure of the graph and is…

Information Theory · Computer Science 2017-05-23 Santiago Segarra , Antonio G. Marques , Alejandro Ribeiro

Graphs are ubiquitous in many applications, such as social networks, knowledge graphs, smart grids, etc.. Graph neural networks (GNN) are the current state-of-the-art for these applications, and yet remain obscure to humans. Explaining the…

Machine Learning · Computer Science 2021-11-22 Yazheng Liu , Xi Zhang , Sihong Xie

We give a near-linear time 4-coloring algorithm for planar graphs, improving on the previous quadratic time algorithm by Robertson et al. from 1996. Such an algorithm cannot be achieved by the known proofs of the Four Color Theorem (4CT).…

This paper contains the proofs of Theorems 2 and 3 of the article entitled Random Electrical Networks on Complete Graphs, written by the same authors and published in the Journal of the London Mathematical Society, vol. 30 (1984), pp.…

Probability · Mathematics 2016-09-07 Geoffrey Grimmett , Harry Kesten

We prove a better coloring theorem for aleph_4 and even aleph_3. This has a general topology consequence.

Logic · Mathematics 2019-01-29 Saharon Shelah

This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations for GNNs. It applies to existing explanation methods,…

Machine Learning · Computer Science 2022-05-25 Zifan Wang , Yuhang Yao , Chaoran Zhang , Han Zhang , Youjie Kang , Carlee Joe-Wong , Matt Fredrikson , Anupam Datta