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Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields.…

Machine Learning · Computer Science 2020-02-12 Uday Shankar Shanthamallu , Jayaraman J. Thiagarajan , Andreas Spanias

Small-world architectures may be implicated in a range of phenomena from disease propagation to networks of neurons in the cerebral cortex. While most of the recent attention on small-world networks has focussed on the effect of introducing…

Disordered Systems and Neural Networks · Physics 2007-05-23 Rajesh Kasturirangan

This study considers online learning with general directed feedback graphs. For this problem, we present best-of-both-worlds algorithms that achieve nearly tight regret bounds for adversarial environments as well as poly-logarithmic regret…

Machine Learning · Computer Science 2022-12-29 Shinji Ito , Taira Tsuchiya , Junya Honda

Graphs naturally appear in several real-world contexts including social networks, the web network, and telecommunication networks. While the analysis and the understanding of graph structures have been a central area of study in algorithm…

Data Structures and Algorithms · Computer Science 2019-09-17 Gramoz Goranci

Network structures are extremely important to the study of political science. Much of the data in its subfields are naturally represented as networks. This includes trade, diplomatic and conflict relationships. The social structure of…

Methodology · Statistics 2011-05-05 Drew Conway

A lot of theoretical and empirical evidence shows that the flatter local minima tend to improve generalization. Adversarial Weight Perturbation (AWP) is an emerging technique to efficiently and effectively find such minima. In AWP we…

Machine Learning · Computer Science 2023-02-21 Yihan Wu , Aleksandar Bojchevski , Heng Huang

The problem of finding a minimum-weight connected dominating set (CDS) of a given undirected graph has been studied actively, motivated by operations of wireless ad hoc networks. In this paper, we formulate a new stochastic variant of the…

Data Structures and Algorithms · Computer Science 2020-01-01 Takuro Fukunaga

With the exponential growth of data and its crucial impact on our lives and decision-making, the integrity of data has become a significant concern. Malicious data poisoning attacks, where false values are injected into the data, can…

Cryptography and Security · Computer Science 2024-03-18 Yue Fu , Qingqing Ye , Rong Du , Haibo Hu

The theory of graphons is an important tool in understanding properties of large networks. We investigate a power-law random graph model and cast it in the graphon framework. The distinctively different structures of the limit graph are…

Probability · Mathematics 2022-08-22 Mei Yin

A weak odd dominated (WOD) set in a graph is a subset B of vertices for which there exists a distinct set of vertices C such that every vertex in B has an odd number of neighbors in C. We point out the connections of weak odd domination…

Computational Complexity · Computer Science 2016-10-11 Sylvain Gravier , Jérôme Javelle , Mehdi Mhalla , Simon Perdrix

A set $S$ of vertices of a graph is a defensive alliance if, for each element of $S$, the majority of its neighbours is in $S$. We consider the notion of local minimality in this paper. We are interested in locally minimal defensive…

Computational Complexity · Computer Science 2023-03-06 Ajinkya Gaikwad , Soumen Maity , Shuvam Kant Tripathi

Dominating sets in graphs are often used to model some monitoring of the graph: guards are posted on the vertices of the dominating set, and they can thus react to attacks occurring on the unguarded vertices by moving there (yielding a new…

Discrete Mathematics · Computer Science 2024-07-16 Guillaume Bagan , Nicolas Bousquet , Nacim Oijid , Théo Pierron

We study the properties of random graphs where for each vertex a {\it neighbourhood} has been previously defined. The probability of an edge joining two vertices depends on whether the vertices are neighbours or not, as happens in Small…

Disordered Systems and Neural Networks · Physics 2009-11-10 Sebastian Risau-Gusman

Differentially private algorithms protect individuals in data analysis scenarios by ensuring that there is only a weak correlation between the existence of the user in the data and the result of the analysis. Dynamic graph algorithms…

Data Structures and Algorithms · Computer Science 2025-09-24 Hendrik Fichtenberger , Monika Henzinger , Lara Ost

Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very large requiring significant computational resources beyond…

An alternating graph is a directed graph whose vertex set is partitioned into two classes, existential and universal. This forms the basic arena for a plethora of infinite duration two-player games where Player~$\square$ and~$\ocircle$…

Data Structures and Algorithms · Computer Science 2025-08-14 Carlo Comin , Romeo Rizzi

Approximate nearest neighbor search (ANN) is a common way to retrieve relevant search results, especially now in the context of large language models and retrieval augmented generation. One of the most widely used algorithms for ANN is…

Data Structures and Algorithms · Computer Science 2025-12-23 Nina Mishra , Yonatan Naamad , Tal Wagner , Lichen Zhang

Connectivity of wireless sensor networks (WSNs) is a fundamental global property expected to be maintained even though some sensor nodes are at fault. In this paper, we investigate the connectivity of random geometric graphs (RGGs) in the…

Information Theory · Computer Science 2018-04-23 Satoshi Takabe , Tadashi Wadayama

In today's enterprise network landscape, the combination of perimeter and distributed firewall rules governs connectivity. To address challenges arising from increased traffic and diverse network architectures, organizations employ…

Cryptography and Security · Computer Science 2026-02-10 Nardine Basta , Firas Ben Hmida , Houssem Jmal , Muhammad Ikram , Mohamed Ali Kaafar , Andy Walker

A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard methodology has a serious flaw - virtually all of the defenses…

Machine Learning · Computer Science 2023-02-01 Felix Mujkanovic , Simon Geisler , Stephan Günnemann , Aleksandar Bojchevski
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