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Graph filters are one of the core tools in graph signal processing. A central aspect of them is their direct distributed implementation. However, the filtering performance is often traded with distributed communication and computational…

Signal Processing · Electrical Eng. & Systems 2019-05-01 Mario Coutino , Elvin Isufi , Geert Leus

A graph is said to be edge-transitive if its automorphism group acts transitively on its edges. It is known that edge-transitive graphs are either vertex-transitive or bipartite. In this paper we present a complete classification of all…

Combinatorics · Mathematics 2019-11-13 Heather A. Newman , Hector Miranda , Darren A. Narayan

Extending the work of Godsil and others, we investigate the notion of the inverse of a graph (specifically, of bipartite graphs with a unique perfect matching). We provide a concise necessary and sufficient condition for the invertibility…

Combinatorics · Mathematics 2011-08-19 Cam McLeman , Erin McNicholas

We demonstrate that a quantum graph exhibits a $\mathcal{PT}$-symmetry provided the coefficients in the condition describing the wave function matching at the vertices are circulant matrices; this symmetry is nontrivial if they are not…

Mathematical Physics · Physics 2021-10-04 Pavel Exner , Milos Tater

Multilevel modeling extends traditional modeling techniques with a potentially unlimited number of abstraction levels. Multilevel models can be formally represented by multilevel typed graphs whose manipulation and transformation are…

Software Engineering · Computer Science 2020-06-26 Uwe Wolter , Fernando Macías , Adrian Rutle

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

Transformer models have recently gained popularity in graph representation learning as they have the potential to learn complex relationships beyond the ones captured by regular graph neural networks. The main research question is how to…

Machine Learning · Computer Science 2023-10-31 Yuankai Luo , Veronika Thost , Lei Shi

Godsil (1985) defined a graph to be invertible if it has a non-singular adjacency matrix whose inverse is diagonally similar to a nonnegative integral matrix; the graph defined by the last matrix is then the inverse of the original graph.…

Combinatorics · Mathematics 2018-10-30 Sona Pavlikova , Daniel Sevcovic

An orientation of a graph is semi-transitive if it contains no directed cycles and has no shortcuts. An undirected graph is semi-transitive if it can be oriented in a semi-transitive manner. The class of semi-transitive graphs includes…

Combinatorics · Mathematics 2024-08-12 Sergey Kitaev , Artem Pyatkin

Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Embeddings are mostly performed on the node level for learning…

Machine Learning · Computer Science 2022-04-26 Nan Wang , Lu Lin , Jundong Li , Hongning Wang

Graph translation is very promising research direction and has a wide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic…

Machine Learning · Computer Science 2021-03-17 Tianxiang Zhao , Xianfeng Tang , Xiang Zhang , Suhang Wang

This paper introduces a new graph construction, the permutational power of a graph, whose adjacency matrix is obtained by the composition of a permutation matrix with the adjacency matrix of the graph. It is shown that this construction…

Combinatorics · Mathematics 2019-10-29 Matteo Cavaleri , Daniele D'Angeli , Alfredo Donno

A directed graph is semi-transitive if and only if it is acyclic and for any directed path $u_1\rightarrow u_2\rightarrow \cdots \rightarrow u_t$, $t \geq 2$, either there is no edge from $u_1$ to $u_t$ or all edges $u_i\rightarrow u_j$…

Combinatorics · Mathematics 2021-10-19 Sergey Kitaev , Artem Pyatkin

One must add arrows which are forced by transitivity to form the transitive closure of a directed graph. We introduce a construction of a transitive directed graph which is formed by adding vertices instead of arrows and which preserves the…

Combinatorics · Mathematics 2012-01-20 Kenneth L. Price

In this article we consider resistance matrix of a connected graph. For unweighted graph we study some necessary and sufficient conditions for resistance regular graphs. Also we find some relationship between Laplacian matrix and resistance…

Combinatorics · Mathematics 2018-03-28 Deepak Sarma

The admittance matrix encodes the network topology and electrical parameters of a power system in order to relate the current injection and voltage phasors. Since admittance matrices are central to many power engineering analyses, their…

Optimization and Control · Mathematics 2026-01-21 Daniel Turizo , Daniel K. Molzahn

Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more efficiency. In this paper, we introduce two graph network architectures that are equivariant to several…

Machine Learning · Computer Science 2021-06-01 Francesco Farina , Emma Slade

This work concerns results on conditions guaranteeing that certain banded $M$-matrices have banded inverses. As a first goal, a graph theoretic characterization for an off-diagonal entry of the inverse of an $M$-matrix to be positive, is…

General Mathematics · Mathematics 2024-12-30 S. Pratihar , K. C. Sivakumar

This paper deals with problem of blind identification of a graph filter and its sparse input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to irregular graph domains. While the…

Signal Processing · Electrical Eng. & Systems 2018-03-14 Chang Ye , Rasoul Shafipour , Gonzalo Mateos

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or…

Machine Learning · Computer Science 2023-08-22 Liheng Ma , Chen Lin , Derek Lim , Adriana Romero-Soriano , Puneet K. Dokania , Mark Coates , Philip Torr , Ser-Nam Lim