Article
Mechanics
Yaron Oz, Ittai Rubinstein, Muli Safra
Summary: We study the spread of information on multi-type directed random graphs and derive an equation for the size of large out-components using multivariate generating functions and multi-type branching processes. Our methods are applied to analyze the spread of epidemics and validated through population-based simulations.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Mathematics, Interdisciplinary Applications
Riccardo Michielan, Clara Stegehuis
Summary: This study investigates the number and type of cliques in a scale-free network model with geometry, proving the existence of a typical clique type and demonstrating a phase transition in terms of the number of cliques and their types based on k and the degree-exponent tau.
JOURNAL OF COMPLEX NETWORKS
(2022)
Article
Mechanics
Ido Tishby, Ofer Biham, Eytan Katzav
Summary: This study presents analytical results for the distribution of first-passage times of random walks on random regular graphs. The first-passage trajectories can be classified into those following the shortest path and those not following the shortest path.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Physics, Fluids & Plasmas
Peter Mann, V. Anne Smith, John B. O. Mitchell, Simon Dobson
Summary: This paper extends analytical solutions to percolation properties of random networks with homogeneous clustering to networks with non-degree equivalent clusters, including multilayer networks. The method presented allows for investigating properties of random complex networks with arbitrary clustering, beyond the traditional configuration model and generating function formulation.
Article
Physics, Fluids & Plasmas
Peter Mann, V. Anne Smith, John B. O. Mitchell, Simon Dobson
Summary: In this paper, percolation theory is used to analyze clustered networks with simple cycles and cliques of any order, providing solutions for critical properties and giant component size of Poisson and power-law networks using the generating function formulation. The study finds that networks with larger simple cycles behave more like trees, while clustering with larger cliques deviates from treelike solutions, influenced by degree-assortativity.
Article
Mathematics
Adrian Marius Deaconu, Delia Spridon
Summary: This paper discusses the adaptation of the Erdos-Renyi model to generate random flow networks, with a developed algorithm based on the decomposition of flows into directed paths and cycles, allowing for quick construction of large-scale networks.
Article
Computer Science, Interdisciplinary Applications
Zahid Halim, Hussain Mahmood Sargana, Aadam, Uzma, Muhammad Waqas
Summary: This paper introduces a random walk-based method to cluster graphs, which uses information of nodes and edges to guide the random walk process and achieve clustering by finding weighted edges and neighboring nodes. Experimental results suggest better performance of this method on evaluation metrics across 18 real-world benchmark datasets.
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Physics, Fluids & Plasmas
Fei Ma, Ping Wang
Summary: The study proposes a simple algorithmic framework for generating power-law graphs with small diameters and examines their structural properties. The results show that these graphs have unique features such as density characteristics and higher trapping efficiency compared to existing scale-free models, confirmed through extensive simulations.
Article
Computer Science, Information Systems
Sateeshkrishna Dhuli, Said Kouachi, Anamika Chhabra, Yatindra Nath Singh
Summary: Internet of Things (IoT) is a collection of smart devices connected to the Internet for various applications. The robustness against the failure of nodes or links is crucial for IoT networks. In this study, we derive the formulas of network criticality for IoT networks using r-nearest neighbor graphs and investigate the effects of nearest neighbors and network size on robustness. Our work significantly reduces computational complexity and reveals that network robustness decreases with network size and exponentially increases with nearest neighbors.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Lucas Guerreiro, Filipi N. Silva, Diego R. Amancio
Summary: Discovery processes in network science focus on knowledge acquisition through exploring nodes. Different learning strategies can lead to the same learning performance, indicating the need to combine learning curves with other sequence features for inferring network topology.
INFORMATION SCIENCES
(2021)
Article
Engineering, Mechanical
C. A. Ramirez-Fuentes, V. Barrera-Figueroa, B. Tovar-Corona, M. A. Silva-Ramirez, L. I. Garay-Jimenez
Summary: This paper presents a methodology based on clinical guidelines for the automatic identification of the epileptic focus and its dynamics using complex networks and linear techniques. By identifying the EEG channels with the most frequent and lasting ictal events, as well as estimating the connectivity parameters of the cerebral networks generated during seizure events, the efficiency of seizure identification has been improved.
NONLINEAR DYNAMICS
(2021)
Article
Mechanics
Ana Vranic, Marija Mitrovic Dankulov
Summary: Network science provides a crucial theoretical framework for studying the structure and function of complex systems. The study explores how the properties of growth signals affect the structure of networks generated, showing that different signals result in varied network structures.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Computer Science, Artificial Intelligence
Bingxin Zhou, Xuebin Zheng, Yu Guang Wang, Ming Li, Junbin Gao
Summary: This paper presents a new graph representation learning scheme called EGG, which preserves the similarity relationship of original graph data in the embedded space and demonstrates superior performance in clustering and classification tasks.
Article
Mechanics
Pawat Akara-pipattana, Thiparat Chotibut, Oleg Evnin
Summary: In this paper, we study the distribution of resistance distances in an Erdos-Renyi random graph and develop an auxiliary field representation for this quantity. Using a 1/c expansion and a saddle point evaluation, we analyze the distribution and find that it exhibits different characteristics at different values of c, including the presence of subleading peaks. A more refined saddle point scheme allows us to analytically recover some of these subleading peaks.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Computer Science, Artificial Intelligence
Chun Wang, Shirui Pan, Celina P. Yu, Ruiqi Hu, Guodong Long, Chengqi Zhang
Summary: Node clustering is a method to partition the vertices in a graph into multiple groups or communities. Existing studies mainly focus on developing deep learning approaches to learn a latent representation of nodes, followed by simple clustering methods like k-means. In this paper, a clustering-directed deep learning approach called DNENC is proposed to encode the topological structure and node content in a graph, and generate soft labels for a self-training process to iteratively refine the node clustering results.
PATTERN RECOGNITION
(2022)
Article
Mechanics
P. L. Krapivsky, J. M. Luck, K. Mallick
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2019)
Article
Physics, Multidisciplinary
Anna S. Bodrova, Vladimir Stadnichuk, P. L. Krapivsky, Juergen Schmidt, Nikolai V. Brilliantov
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2019)
Article
Mechanics
P. L. Krapivsky, J. M. Luck
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2019)
Article
Mechanics
P. L. Krapivsky, L. Nazarov, M. Tamm
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2019)
Article
Mechanics
P. L. Krapivsky, S. Redner
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2019)
Article
Mechanics
P. L. Krapivsky, Kirone Mallick, Dries Sels
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2019)
Article
Mechanics
P. L. Krapivsky, J. M. Luck
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2020)
Article
Mechanics
P. L. Krapivsky, Kirone Mallick, Dries Sels
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2020)
Article
Mechanics
P. L. Krapivsky, S. Redner
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2020)
Article
Physics, Fluids & Plasmas
N. Brilliantov, A. Osinsky, P. L. Krapivsky
Article
Physics, Fluids & Plasmas
Ginestra Bianconi, P. L. Krapivsky
Article
Physics, Fluids & Plasmas
E. Ben-Naim, P. L. Krapivsky
Article
Physics, Fluids & Plasmas
Keming Zhang, P. L. Krapivsky, S. Redner
Article
Physics, Fluids & Plasmas
Tal Agranov, P. L. Krapivsky, Baruch Meerson
Article
Physics, Fluids & Plasmas
P. L. Krapivsky