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In this work, we investigate the relationship between $k$-repre\-sentable graphs and graphs representable by $k$-local words. In particular, we show that every graph representable by a $k$-local word is $(k+1)$-representable. A previous…

Combinatorics · Mathematics 2025-06-25 Philipp Böll , Pamela Fleischmann , Annika Huch , Jana Kreiß , Tim Löck , Kajus Park , Max Wiedenhöft

{\em Partial domination problem} is a generalization of the {\em minimum dominating set problem} on graphs. Here, instead of dominating all the nodes, one asks to dominate at least a fraction of the nodes of the given graph by choosing a…

Computational Geometry · Computer Science 2025-05-23 Madhura Dutta , Anil Maheshwari , Subhas C. Nandy , Bodhayan Roy

The independence gap of a graph was introduced by Ekim et al. (2018) as a measure of how far a graph is from being well-covered. It is defined as the difference between the maximum and minimum size of a maximal independent set. We…

Combinatorics · Mathematics 2018-12-14 Tınaz Ekim , Didem Gözüpek , Ademir Hujdurović , Martin Milanič

In a graph G, a k-attack A is any set of at most k vertices and l-defense D is a set of at most l vertices. We say that defense D counters attack A if each a in A can be matched to a distinct defender d in D with a equal to d or a adjacent…

Computational Complexity · Computer Science 2025-10-02 Steven Chaplick , Grzegorz Gutowski , Tomasz Krawczyk

A linear graph is a graph whose vertices are totally ordered. Biological and linguistic sequences with interactions among symbols are naturally represented as linear graphs. Examples include protein contact maps, RNA secondary structures…

Data Structures and Algorithms · Computer Science 2011-03-08 Yasuo Tabei , Daisuke Okanohara , Shuichi Hirose , Koji Tsuda

We consider the problem of learning a causal graph over a set of variables with interventions. We study the cost-optimal causal graph learning problem: For a given skeleton (undirected version of the causal graph), design the set of…

Artificial Intelligence · Computer Science 2017-03-09 Murat Kocaoglu , Alexandros G. Dimakis , Sriram Vishwanath

Treewidth is an important and well-known graph parameter that measures the complexity of a graph. The Kneser graph Kneser(n,k) is the graph with vertex set $\binom{[n]}{k}$, such that two vertices are adjacent if they are disjoint. We…

Combinatorics · Mathematics 2015-06-08 Daniel J. Harvey , David R. Wood

We introduce $k$-robust clique complexes, a family of simplicial complexes that generalizes the traditional clique complex. Here, a subset of vertices forms a simplex provided it does not contain an independent set of size $k$. We…

Combinatorics · Mathematics 2026-04-02 Marek Filakovský

Let G be a simple graph without isolated vertices. For a vertex i in G, the degree d_i is the number of vertices adjacent to i and the average 2-degree m_i is the mean of the degrees of the vertices which are adjacent to i. The sequence of…

Combinatorics · Mathematics 2018-11-08 Yu-pei Huang , Chia-an Liu , Chih-wen Weng

The definition of $1$-planar graphs naturally extends graph planarity, namely a graph is $1$-planar if it can be drawn in the plane with at most one crossing per edge. Unfortunately, while testing graph planarity is solvable in linear time,…

Computational Geometry · Computer Science 2019-11-05 Carla Binucci , Walter Didimo , Fabrizio Montecchiani

Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph…

Machine Learning · Computer Science 2021-08-20 Reyan Ahmed , Md Asadullah Turja , Faryad Darabi Sahneh , Mithun Ghosh , Keaton Hamm , Stephen Kobourov

Geometric Graph Neural Networks (GNNs) and Transformers have become state-of-the-art for learning from 3D protein structures. However, their reliance on message passing prevents them from capturing the hierarchical interactions that govern…

Machine Learning · Computer Science 2025-12-09 Chang Liu , Vivian Li , Linus Leong , Vladimir Radenkovic , Pietro Liò , Chaitanya K. Joshi

We introduce De Bruijn Graph Neural Networks (DBGNNs), a novel time-aware graph neural network architecture for time-resolved data on dynamic graphs. Our approach accounts for temporal-topological patterns that unfold in the causal topology…

Machine Learning · Computer Science 2022-09-20 Lisi Qarkaxhija , Vincenzo Perri , Ingo Scholtes

We strengthen a result by Laskar and Lyle (Discrete Appl. Math. (2009), 330-338) by proving that it is NP-complete to decide whether a bipartite planar graph can be partitioned into three independent dominating sets. In contrast, we show…

Computational Complexity · Computer Science 2019-05-14 Juho Lauri , Christodoulos Mitillos

We first characterise graphs with binomial edge ideals of K\"onig type as those for which the path covering number is equal to a minor variant of the scattering number. These are well-studied graph-theoretic invariants, allowing us to apply…

Commutative Algebra · Mathematics 2026-05-26 David Williams

Properties of graphs that can be characterized by the spectrum of the adjacency matrix of the graph have been studied systematically recently. Motivated by the complexity of these properties, we show that there are such properties for which…

Combinatorics · Mathematics 2020-01-28 Omid Etesami , Willem H. Haemers

A $k$-independent set in a connected graph is a set of vertices such that any two vertices in the set are at distance greater than $k$ in the graph. The $k$-independence number of a graph, denoted $\alpha_k$, is the size of a largest…

Combinatorics · Mathematics 2023-05-01 Lord C. Kavi , Mike Newman

A graph is beyond-planar if it can be drawn in the plane with a specific restriction on crossings. Several types of beyond-planar graphs have been investigated, such as k-planar if every edge is crossed at most k times and RAC if edges can…

Discrete Mathematics · Computer Science 2022-01-04 Franz J. Brandenburg

We consider a variant of so called power-law random graph. A sequence of expected degrees corresponds to a power-law degree distribution with finite mean and infinite variance. In previous works the asymptotic picture with number of nodes…

Probability · Mathematics 2007-12-12 Hannu Reittu , Ilkka Norros

Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes. However, DGNs are typically limited in…

Machine Learning · Computer Science 2023-02-09 Alessio Gravina , Davide Bacciu , Claudio Gallicchio
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