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Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a mixture of nodes with different neighbor patterns,…

Machine Learning · Computer Science 2025-02-26 Jinluan Yang , Zhengyu Chen , Teng Xiao , Wenqiao Zhang , Yong Lin , Kun Kuang

Graph transformation is the rule-based modification of graphs, and is a discipline dating back to the 1970s. In general, to match the left-hand graph of a fixed rule within a host graph requires polynomial time, but to improve matching…

Logic in Computer Science · Computer Science 2021-01-05 Graham Campbell , Detlef Plump

Let $G$ be a finite tree with root $r$ and associate to the internal vertices of $G$ a collection of transition probabilities for a simple nondegenerate Markov chain. Embedd $G$ into a graph $G^\prime$ constructed by gluing finite linear…

Probability · Mathematics 2007-05-23 Victor de la Pena , Henryk Gzyl , Patrick McDonald

This paper is centered on the random graph generated by a Doeblin-type coupling of discrete time processes on a countable state space whereby when two paths meet, they merge. This random graph is studied through a novel subgraph, called a…

Probability · Mathematics 2018-11-27 François Baccelli , Mir-Omid Haji-Mirsadeghi , James T. Murphy

Random graphs with a given degree sequence are often constructed using the configuration model, which yields a random multigraph. We may adjust this multigraph by a sequence of switchings, eventually yielding a simple graph. We show that,…

Probability · Mathematics 2019-02-01 Svante Janson

The paper considers a distributed robust estimation problem over a network with Markovian randomly varying topology. The objective is to deal with network variations locally, by switching observer gains at affected nodes only. We propose…

Systems and Control · Computer Science 2015-12-07 V. Ugrinovskii

In our recent works, we developed a probabilistic framework for structural analysis in undirected networks. The key idea of that framework is to sample a network by a symmetric bivariate distribution and then use that bivariate distribution…

Social and Information Networks · Computer Science 2015-10-19 Cheng-Shang Chang , Duan-Shin Lee , Li-Heng Liou , Sheng-Min Lu , Mu-Huan Wu

This paper is centered on covariant dynamics on unimodular random graphs and random networks, namely maps from the set of vertices to itself which are preserved by graph or network isomorphisms. Such dynamics are referred to as…

Probability · Mathematics 2020-01-10 François Baccelli , Mir-Omid Haji-Mirsadeghi , Ali Khezeli

Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point $\tau$, a change occurs at a subset of nodes $C$, which affects the probability distribution of their…

Machine Learning · Statistics 2023-01-13 Alejandro de la Concha , Argyris Kalogeratos , Nicolas Vayatis

Let $G$ be a graph in which each vertex initially has weight 1. In each step, the unit weight from a vertex $u$ to a neighbouring vertex $v$ can be moved, provided that the weight on $v$ is at least as large as the weight on $u$. The unit…

Combinatorics · Mathematics 2020-06-25 Konstantinos Georgiou , Somnath Kundu , Pawel Pralat

A permutation graph is a graph whose edges are given by inversions of a permutation. We study the Abelian sandpile model (ASM) on such graphs. We exhibit a bijection between recurrent configurations of the ASM on permutation graphs and the…

Combinatorics · Mathematics 2018-10-08 Mark Dukes , Thomas Selig , Jason P. Smith , Einar Steingrimsson

We consider unimodular random rooted trees (URTs) and invariant forests in Cayley graphs. We show that URTs of bounded degree are the same as the law of the component of the root in an invariant percolation on a regular tree. We use this to…

Probability · Mathematics 2019-02-20 Itai Benjamini , Russell Lyons , Oded Schramm

In this paper, we study Dirac-type theorems for an inhomogenous random graph (G) whose edge probabilities are not necessarily all the same. We obtain sufficient conditions for the existence of Hamiltonian paths and perfect matchings, in…

Probability · Mathematics 2024-04-04 Ghurumuruhan Ganesan

We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical subgraph densities are maximum likelihood estimates of their…

Statistics Theory · Mathematics 2017-11-22 Steffen Lauritzen , Alessandro Rinaldo , Kayvan Sadeghi

Bispanning graphs are undirected graphs with an edge set that can be decomposed into two disjoint spanning trees. The operation of symmetrically swapping two edges between the trees, such that the result is a different pair of disjoint…

Combinatorics · Mathematics 2016-05-12 Timo Bingmann

We offer a solution to a long-standing problem in the physics of networks, the creation of a plausible, solvable model of a network that displays clustering or transitivity -- the propensity for two neighbors of a network node also to be…

Statistical Mechanics · Physics 2009-08-13 M. E. J. Newman

Let \Pi be an ergodic simple point process on R^d and let \Pi^* be its Palm version. Thorisson [Ann. Probab. 24 (1996) 2057-2064] proved that there exists a shift coupling of \Pi and \Pi^*; that is, one can select a (random) point Y of \Pi…

Probability · Mathematics 2007-05-23 Alexander E. Holroyd , Yuval Peres

We consider the distribution of cycle counts in a random regular graph, which is closely linked to the graph's spectral properties. We broaden the asymptotic regime in which the cycle counts are known to be approximately Poisson, and we…

Combinatorics · Mathematics 2023-07-14 Tobias Johnson

In this paper, we study a bipartite analogue of the `random graphs evolving by degrees' process. We are given a bipartitioned set of vertices $V$ into two disjoint parts ${L}$ and ${R}$ and possibly unequal positive constants $\alpha$ and…

Probability · Mathematics 2025-09-30 Neeladri Maitra

Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements. In this…

Machine Learning · Statistics 2021-04-13 Jesús Arroyo , Carey E. Priebe , Vince Lyzinski