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相关论文: Accurately modeling the Internet topology

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In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of…

机器学习 · 计算机科学 2023-07-04 Antonio Briola , Tomaso Aste

In this paper we present a framework for the extension of the preferential attachment (PA) model to heterogeneous complex networks. We define a class of heterogeneous PA models, where node properties are described by fixed states in an…

其他凝聚态物理 · 物理学 2009-11-13 A. Santiago , R. M. Benito

Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort…

机器学习 · 统计学 2023-02-20 Cheng Zhang

A Peer-to-Peer (P2P) network can boost its performance if peers are provided with underlying network-layer routing topology. The task of inferring the network-layer routing topology and link performance from an end host to a set of other…

网络与互联网体系结构 · 计算机科学 2014-04-01 Peng Qin , Bin Dai , Kui Wu , Benxiong Huang , Guan Xu

The physical topology is emerging as the next frontier in an ongoing effort to render communication networks more flexible. While first empirical results indicate that these flexibilities can be exploited to reconfigure and optimize the…

网络与互联网体系结构 · 计算机科学 2018-07-10 Chen Avin , Stefan Schmid

A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i.e., row and column sums). Prior approaches to this…

机器学习 · 统计学 2024-08-21 Serina Chang , Frederic Koehler , Zhaonan Qu , Jure Leskovec , Johan Ugander

Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms…

社会与信息网络 · 计算机科学 2021-04-21 Naomi A. Arnold , Raul J. Mondragon , Richard G. Clegg

The Saccharomyces cerevisiae protein-protein interaction map, as well as many natural and man-made networks, shares the scale-free topology. The preferential attachment model was suggested as a generic network evolution model that yields…

统计力学 · 物理学 2007-05-23 Eli Eisenberg , Erez Y. Levanon

The importance of modeling and analyzing Social Networks is a consequence of the success of Online Social Networks during last years. Several models of networks have been proposed, reflecting the different characteristics of Social…

社会与信息网络 · 计算机科学 2012-02-03 Emilio Ferrara , Giacomo Fiumara

Our work introduces an approach for estimating the contribution of attachment mechanisms to the formation of growing networks. We present a generic model in which growth is driven by the continuous attachment of new nodes according to…

概率论 · 数学 2019-02-20 Jan Medina , Jorge Finke , Camilo Rocha

We present a statistical analysis of different metrics characterizing the topological properties of Internet maps, collected at two different resolution scales: the router and the autonomous system level. The metrics we consider allow us to…

无序系统与神经网络 · 物理学 2007-05-23 A. Vazquez , R. Pastor-Satorras , A. Vespignani

Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge about real-world mechanisms governing network growth or may…

社会与信息网络 · 计算机科学 2025-12-04 Maxwell H Wang , Till Hoffmann , Jukka-Pekka Onnela

We propose Alternating Phase-Field Fourier Neural Networks (APF-FNNs) as a unified and physics-based framework for topology optimization. The approach decouples the design problem by representing the state, adjoint, and topology fields with…

最优化与控制 · 数学 2026-01-27 Jing Li , Xindi Hu , Helin Gong , Wei Gong , Shengfeng Zhu

We show that the Internet topology at the Autonomous System (AS) level has a rich--club phenomenon. The rich nodes, which are a small number of nodes with large numbers of links, are very well connected to each other. The rich--club is a…

网络与互联网体系结构 · 计算机科学 2007-05-23 Shi Zhou , Raul J Mondragon

There has been considerable recent interest in the properties of networks, such as citation networks and the worldwide web, that grow by the addition of vertices, and a number of simple solvable models of network growth have been studied.…

统计力学 · 物理学 2011-11-09 Cristopher Moore , Gourab Ghoshal , M. E. J. Newman

The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions--two typical…

社会与信息网络 · 计算机科学 2013-09-02 Menghui Li , Hailin Zou , Shuguang Guan , Xiaofeng Gong , Kun Li , Zengru Di , Choy-Heng Lai

We study the approximate fixed point property (AFPP) for continuous single-valued functions and for continuous multivalued functions in digital topology. We extend what is known about these notions and discuss errors that have appeared in…

一般拓扑 · 数学 2020-01-20 Laurence Boxer

Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model. Here we introduce a new…

A major achievement in the study of complex networks is the observation that diverse systems, from sub-cellular biology to social networks, exhibit universal topological characteristics. Yet this universality does not naturally translate to…

物理与社会 · 物理学 2018-01-29 Chittaranjan Hens , Uzi Harush , Reuven Cohen , Baruch Barzel

Inspired by scientific collaboration networks, especially our empirical analysis of the network of econophysicists, an evolutionary model for weighted networks is proposed. Both degree-driven and weight-driven models are considered.…

无序系统与神经网络 · 物理学 2007-05-23 Menghui Li , Jinshan Wu , Dahui Wang , Tao Zhou , Zengru Di , Ying Fan
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