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Most of the machine learning algorithms are limited to learn from flat data: a recordset with prefixed structure. When learning from a record, these types of algorithms don't take into account other objects even though they are directly…

Databases · Computer Science 2017-08-15 Pedro Almagro-Blanco , Fernando Sancho-Caparrini

Let $G=(V,E)$ be a graph. An ordering of $G$ is a bijection $\alpha: V\dom \{1,2,..., |V|\}.$ For a vertex $v$ in $G$, its closed neighborhood is $N[v]=\{u\in V: uv\in E\}\cup \{v\}.$ The profile of an ordering $\alpha$ of $G$ is…

Data Structures and Algorithms · Computer Science 2007-05-23 Gregory Gutin , Stefan Szeider , Anders Yeo

Many real-world phenomena are naturally modeled by graphs and networks. However, classical graph models are often limited to pairwise interactions and may not adequately capture the richer structures that arise in practice. Higher-order…

Social and Information Networks · Computer Science 2026-05-18 Takaaki Fujita , Florentin Smarandache

Complex networks are everywhere. They appear for example in the form of biological networks, social networks, or computer networks and have been studied extensively. Efficient algorithms to solve problems on complex networks play a central…

Discrete Mathematics · Computer Science 2020-06-26 Jan Dreier , Philipp Kuinke , Peter Rossmanith

The complicated syntax structure of natural language is hard to be explicitly modeled by sequence-based models. Graph is a natural structure to describe the complicated relation between tokens. The recent advance in Graph Neural Networks…

Computation and Language · Computer Science 2019-09-19 Wei Li , Shuheng Li , Shuming Ma , Yancheng He , Deli Chen , Xu Sun

Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many real-world problems, the graph expands over time as new nodes…

Machine Learning · Computer Science 2019-11-27 Arun Venkitaraman , Saikat Chatterjee , Bo Wahlberg

Graph Interpolation Grammars are a declarative formalism with an operational semantics. Their goal is to emulate salient features of the human parser, and notably incrementality. The parsing process defined by GIGs incrementally builds a…

cmp-lg · Computer Science 2009-09-25 John Larcheveque

We present a graph neural network model for solving graph-to-graph learning problems. Most deep learning on graphs considers ``simple'' problems such as graph classification or regressing real-valued graph properties. For such tasks, the…

Machine Learning · Computer Science 2021-06-08 Guan Wang , Francois Bernard Lauze , Aasa Feragen

The Gruenberg-Kegel graph of a group is the undirected graph whose vertices are those primes which occur as the order of an element of the group, and distinct vertices $p$, $q$ are joined by an edge whenever the group has an element of…

Group Theory · Mathematics 2026-04-07 Andreas Bächle , Ann Kiefer , Sugandha Maheshwary , Ángel del Río

The codegree of an irreducible character $\chi$ of a finite group $G$ is defined as $|G:\ker\chi|/\chi(1)$. The codegree graph $\Gamma(G)$ of a finite group $G$ is the graph whose vertices are the prime divisors of $|G|$, where two distinct…

Group Theory · Mathematics 2025-10-20 Jiyong Chen , Ni Du , Leyi Li

We introduce graph pattern-based association rules (GPARs) for directed labeled multigraphs such as RDF graphs. GPARs support both generative tasks, where a graph is extended, and evaluative tasks, where the plausibility of a graph is…

Databases · Computer Science 2025-12-18 Basil Ell

Scott's graph model is a lambda-algebra based on the observation that continuous endofunctions on the lattice of sets of natural numbers can be represented via their graphs. A graph is a relation mapping finite sets of input values to…

Logic in Computer Science · Computer Science 2015-07-01 Guy McCusker

When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable challenge for the model in accurately capturing authentic…

Machine Learning · Computer Science 2024-06-14 Hang Gao , Peng Qiao , Yifan Jin , Fengge Wu , Jiangmeng Li , Changwen Zheng

We consider Hoare-style verification for the graph programming language GP 2. In previous work, graph properties were specified by so-called E-conditions which extend nested graph conditions. However, this type of assertions is not easy to…

Logic in Computer Science · Computer Science 2020-12-04 Gia S. Wulandari , Detlef Plump

We develop a new framework for generalizing approximation algorithms from the structural graph algorithm literature so that they apply to graphs somewhat close to that class (a scenario we expect is common when working with real-world…

An upper dominating set in a graph is a minimal (with respect to set inclusion) dominating set of maximum cardinality. The problem of finding an upper dominating set is generally NP-hard. We study the complexity of this problem in classes…

Discrete Mathematics · Computer Science 2016-09-07 Hassan AbouEisha , Shahid Hussain , Vadim Lozin , Jérôme Monnot , Bernard Ries , Viktor Zamaraev

Directed acyclic graphs whose nodes are all the divisors of a positive integer $n$ and arcs $(a,b)$ defined by $a$ divides $b$ are considered. Fourteen graph invariants such as order, size, and the number of paths are investigated for two…

Number Theory · Mathematics 2014-05-22 Sung-Hyuk Cha , Edgar G. DuCasse , Louis V. Quintas

Let $G$ be a prolific graph, that is a finite connected simple graph which is not isomorphic to a cycle nor a path nor the star graph $K_{1,3}$. The line-graph of $G$, denoted by $L(G)$, is defined by having its vertex-set equal to the…

Combinatorics · Mathematics 2022-01-14 Yair Caro , Josef Lauri , Christina Zarb

We investigate (2,1):1 structures, which consist of a countable set $A$ together with a function $f: A \to A$ such that for every element $x$ in $A$, $f$ maps either exactly one element or exactly two elements of $A$ to $x$. These…

Logic · Mathematics 2017-01-06 Hakim J. Walker

We consider first order expressible properties of random perfect graphs. That is, we pick a graph $G_n$ uniformly at random from all (labelled) perfect graphs on $n$ vertices and consider the probability that it satisfies some graph…

Combinatorics · Mathematics 2018-10-02 Tobias Müller , Marc Noy
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