Various real-world networks interact with and depend on each other. The design of the interconnection between interacting networks is one of the main challenges to achieve a robust interdependent network. Due to cost considerations, network providers are inclined to interconnect nodes that are geographically close. Accordingly, we propose two topologies, the random geographic graph and the relative neighborhood graph, for the design of interconnection in interdependent networks that incorporates the geographic location of nodes. Differing from the one-to-one interconnection studied in the literature, one node in one network can depend on an arbitrary number of nodes in the other network. We derive the average number of interdependent links for the two topologies, which enables their comparison. For the two topologies, we evaluate the impact of the interconnection structure on the robustness of interdependent networks against cascading failures. The two topologies are assessed on the real-world coupled Italian Internet and the electric transmission network. Finally, we propose the derivative of the largest mutually connected component with respect to the fraction of failed nodes as a robustness metric. This robustness metric quantifies the damage of the network introduced by a small fraction of initial failures well before the critical fraction of failures at which the whole network collapses.
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http://dx.doi.org/10.1103/PhysRevE.94.042315 | DOI Listing |
Sci Rep
January 2025
Departemant of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran.
With careful design and integration, microring resonators can serve as a promising foundation for developing compact and scalable sources of non-classical light for quantum information processing. However, the current design flow is hindered by computational challenges and a complex, high-dimensional parameter space with interdependent variables. In this work, we present a knowledge-integrated machine learning framework based on Bayesian Optimization for designing squeezed light sources using microring resonators.
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January 2025
Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China.
Graph neural networks (GNNs) have emerged as a prominent approach for capturing graph topology and modeling vertex-to-vertex relationships. They have been widely used in pattern recognition tasks including node and graph label prediction. However, when dealing with graphs from non-Euclidean domains, the relationships, and interdependencies between objects become more complex.
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December 2024
Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address:
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neural network for event node representation learning.
View Article and Find Full Text PDFMed Anthropol Q
December 2024
Department of Anthropology, Johns Hopkins University, Baltimore, USA.
Attending closely to the lived experiences of people moving in and out of Medicaid-funded institutions, I argue that "the streets" are critical to understanding healthcare in US urban poverty. Exploring the relationship between "the streets" and Medicaid-funded institutions, this essay asks: How does the relationship between "the streets"-and in the words of my research interlocutors-"life on the other side" shape life in Medicaid-funded institutions in the Northeast US city? How do the social and symbolic conditions of this relationship-conditions structured by anti-Blackness-formulate the human in urban poverty? By joining Medicaid-funded institutions together as a broader health-governing network, I demonstrate how these institutions become boundary spaces that reveal the socially and symbolically interdependent worlds of "the streets" and life off them. Ultimately, this essay argues that "the streets" contain the social and symbolic conditions that dehumanize the poor through the logics of anti-Blackness, thus defining the terms of humanization that Medicaid-funded institutions afford.
View Article and Find Full Text PDFBiom J
February 2025
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop four network autocorrelation models for a binary random variable defined by whether the peer effect (also termed social influence or contagion) acts on latent continuous outcomes leading to an indirect effect under a normal or a logistic distribution or on the probability of the observed outcome itself under a probit or a logit link function defining a direct effect to account for interdependence between outcomes. For all models, we use a Bayesian approach for model estimation under a uniform prior on a transformed peer effect parameter ( ) designed to enhance model computation and compare results to those under the uniform prior for .
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