Top Research Papers on Networking

Current GNN-based methods are reviewed, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification, and charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.

This work discusses briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks.

The SDN is reviewed; it introduces SDN, explaining its core concepts, how it varies from traditional networking, and its architecture principles, and the crucial advantages and challenges of SDN are presented, focusing on scalability, security, flexibility, and performance.

An artificial intelligence (AI)-native network slicing architecture for 6G networks is presented to enable the synergy of AI and network slicing, thereby facilitating intelligent network management and supporting emerging AI services.

An algorithm named Network Embedding for node Clustering (NEC) to learn network embedding for node clustering in attributed graphs and introduces soft modularity, which can be easily optimized using gradient descent algorithms, to exploit the community structure of networks.

This minireview provides an overview of the recent developments of bioinspired DN hydrogels defined as DN hydhydrogels that mimic the properties and/or structure of natural tissue, ranging from, e.g., anisotropically structured DNHydrogels, via ultratough energy dissipating DN Hydrogels to dynamic, reshapable DN hyd rogels.

This tutorial paper looks into the evolution and prospect of network architecture and proposes a novel conceptual architecture for the 6th generation (6G) networks, which can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI.

An overview of the construction method of wireless communication graph for various wireless networks and the progress of several classical paradigms of graph neural networks are introduced, as well as several applications of GNNs in wireless networks such as resource allocation and several emerging fields.

This paper makes the first attempt to study network schema preserving HIN embedding, and proposes a novel model named NSHE, which significantly outperforms the state-of-the-art methods.

The Network Society is now more than ever the essential guide to the past, consequences and future of digital communication. Fully revised, this Third Edition covers crucial new issues and updates. This book remains an accessible, comprehensive, must-read introduction to how new media function in contemporary society.

The question of agency has been neglected in social network research, in part because the structural approach to social relations removes consideration of individual volition and action. However, recent emphasis on purposive individuals has reignited interest in agency across a range of social network research topics. Our paper provides a brief history of social network agency and an emergent framework based on a thorough review of research published since 2004. This organizing framework distinguishes between an ontology of dualism (actors and social relations as separate domains) and an ontol...

This paper presents the Discrete Case: Multinomial Bayesian Networks and the Continuous Case: Gaussian Bayesian networks, both of which areagnostic of the discrete and continuous cases.

This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure, and discusses some ambiguous definitions, including robustness, resilience, and stability.

This Review Article summarizes progress in network geometry, its theory, and applications to biological, sociotechnical and other real-world networks and offers perspectives on future research directions and challenges in this frontier in the study of complexity.

The fitness landscape analysis verifies that the transformed community detection problems have smoother landscapes than those of the original problems, which justifies the effectiveness of the proposed graph neural network encoding method.

This survey presents a comprehensive analysis of the exploitation of network slicing in IoT realisation and discusses the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning (AI/ML) in network slicing and IoT integration.

This work presents BrainGB, a benchmark for brain network analysis with GNNs, a standardizes the process by summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and modularizing the implementation of GNN designs.

The proposed NFN＋ model, to the best knowledge, achieved the state-of-the-art retinal vessel segmentation accuracy on color fundus images (AUC: 98.30%, 98.75% and 98.94%, respectively).

Gene regulatory networks (GRNs) encode the complex molecular interactions that govern cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer GRNs and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data. In particular, we developed a neural network version of the structural equation model (SEM) to explicitly model the regulatory relationships among genes. Benchmark results show that DeepSEM achieves comparable or better performance on a variety of single-cell computational tasks, such as GRN inference, scRNA-seq data visualizatio...

It has been proposed to move from hypotheses supporting an overall hemispheric specialization for emotion processing toward dynamic models incorporating multiple interrelated networks which do not necessarily share the same lateralization patterns.