Modern scientific fields face the challenge of integrating a wealth of data, analyses, and results. We recently showed that a neglect of this integration can lead to circular analyses and redundant explanations. Here, we help advance scientific integration by describing equivalences that unify diverse analyses of datasets and networks. We describe equivalences across analyses of clustering and dimensionality reduction, network centrality and dynamics, and popular models in imaging and network neuroscience. First, we equate foundational objectives across unsupervised learning and network science (from k means to modularity to UMAP), fuse classic algorithms for optimizing these objectives, and extend these objectives to simplify interpretations of popular dimensionality reduction methods. Second, we equate basic measures of connectional magnitude and dispersion with six measures of communication, control, and diversity in network science and network neuroscience. Third, we describe three semi-analytical vignettes that clarify and simplify the interpretation of structural and dynamical analyses in imaging and network neuroscience. We illustrate our results on example brain-imaging dat
Endowing brain anatomy, dynamics, and function with a network structure is becoming standard in neuroscience. In its simplest form, a network is a collection of units and relationships between them. The pattern of relations among the units encodes numerous properties which have been shown to have a profound effect on networked systems' dynamics and function. In an effort to strike a balance between idealization and detail, network neuroscience studies typically involve simplifying assumptions at both neural and network modeling levels. However, the extent to which existing neural models depend on such approximations is as yet poorly understood. Here, we discuss whether and how increasing neurophysiological detail and generalizing the basic simple network structure often adopted in network neuroscience may help improve our understanding of brain phenomenology and function.
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and to identify principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modeling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, utility in intuiting mechanisms, and a short history on their use in network science broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including \emph{C. elegans}, \
Similarity analyses between multiple correlation or covariance tables constitute the cornerstone of network neuroscience. Here, we introduce covSTATIS, a versatile, linear, unsupervised multi-table method designed to identify structured patterns in multi-table data, and allow for the simultaneous extraction and interpretation of both individual and group-level features. With covSTATIS, multiple similarity tables can now be easily integrated, without requiring a priori data simplification, complex black-box implementations, user-dependent specifications, or supervised frameworks. Applications of covSTATIS, a tutorial with Open Data and source code are provided. CovSTATIS offers a promising avenue for advancing the theoretical and analytic landscape of network neuroscience.
The sixth generation mobile communication standard (6G) can promote the development of Industrial Internet and Internet of Things (IoT). To achieve comprehensive intelligent development of the network and provide customers with higher quality personalized services. This paper proposes a network performance optimization and intelligent operation network architecture based on Large Language Model (LLM), aiming to build a comprehensive intelligent 6G network system. The Large Language Model, with more parameters and stronger learning ability, can more accurately capture patterns and features in data, which can achieve more accurate content output and high intelligence and provide strong support for related research such as network data security, privacy protection, and health assessment. This paper also presents the design framework of a network health assessment system based on LLM and focuses on its potential application value, through the case of network health management system, it is fully demonstrated that the 6G intelligent network system based on LLM has important practical significance for the comprehensive realization of intelligence.
The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances when this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear - for example, heteroclinic network attractors. In this review we present a set of mathematical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical framework for further successful applications of mathematics to understanding network dynamics in neuroscience.
Connectomics and network neuroscience offer quantitative scientific frameworks for modeling and analyzing networks of structurally and functionally interacting neurons, neuronal populations, and macroscopic brain areas. This shift in perspective and emphasis on distributed brain function has provided fundamental insight into the role played by the brain's network architecture in cognition, disease, development, and aging. In this chapter, we review the core concepts of human connectomics at the macroscale. From the construction of networks using functional and diffusion MRI data, to their subsequent analysis using methods from network neuroscience, this review highlights key findings, commonly-used methodologies, and discusses several emerging frontiers in connectomics.
From interacting cellular components to networks of neurons and neural systems, interconnected units comprise a fundamental organizing principle of the nervous system. Understanding how their patterns of connections and interactions give rise to the many functions of the nervous system is a primary goal of neuroscience. Recently, this pursuit has begun to benefit from the development of new mathematical tools that can relate a system's architecture to its dynamics and function. These tools, which are known collectively as network science, have been used with increasing success to build models of neural systems across spatial scales and species. Here we discuss the nature of network models in neuroscience. We begin with a review of model theory from a philosophical perspective to inform our view of networks as models of complex systems in general, and of the brain in particular. We then summarize the types of models that are frequently studied in network neuroscience along three primary dimensions: from data representations to first-principles theory, from biophysical realism to functional phenomenology, and from elementary descriptions to coarse-grained approximations. We then cons
With the advent of quantum computing, the increasing threats to security poses a great challenge to communication networks. Recent innovations in this field resulted in promising technologies such as Quantum Key Distribution (QKD), which enables the generation of unconditionally secure keys, establishing secure communications between remote nodes. Additionally, QKD networks enable the interconnection of multinode architectures, extending the point-to-point nature of QKD. However, due to the limitations of the current state of technology, the scalability of QKD networks remains a challenge toward feasible implementations. When it comes to long-distance implementations, trusted relay nodes partially solve the distance issue through the forwarding of the distributed keys, allowing applications that do not have a direct QKD link to securely share key material. Even though the relay procedure itself has been extensively studied, the establishment of the relaying node path still lacks a solution. This paper proposes an innovative network architecture that solves the challenges of Key Management System (KMS) identification, relay path discovery, and scalability of QKD networks by integrat
5G networks support various advanced applications through network slicing, network function virtualization (NFV), and edge computing, ensuring low latency and service isolation. However, private 5G networks relying on open-source tools still face challenges in maturity and integration with edge/cloud platforms, compromising proper slice isolation. This study investigates resource allocation mechanisms to address this issue, conducting experiments in a hospital scenario with medical video conferencing. The results show that CPU limitations improve the performance of prioritized slices, while memory restrictions have minimal impact. The generated data and scripts have been made publicly available for future research and machine learning applications.
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.
The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.
Blockchains are typically managed by peer-to-peer (P2P) networks providing the support and substrate to the so-called distributed ledger (DLT), a replicated, shared, and synchronized data structure, geographically spread across multiple nodes. The Bitcoin (BTC) blockchain is by far the most well known DLT, used to record transactions among peers, based on the BTC digital currency. In this paper, we focus on the network side of the BTC P2P network, analyzing its nodes from a purely network measurements-based approach. We present a BTC crawler able to discover and track the BTC P2P network through active measurements, and use it to analyze its main properties. Through the combined analysis of multiple snapshots of the BTC network as well as by using other publicly available data sources on the BTC network and DLT, we unveil the BTC P2P network, locate its active nodes, study their performance, and track the evolution of the network over the past two years. Among other relevant findings, we show that (i) the size of the BTC network has remained almost constant during the last 12 months - since the major BTC price drop in early 2018, (ii) most of the BTC P2P network resides in US and E
Global optimization of access point (AP) assignment to user terminals requires efficient monitoring of user behavior, fast decision algorithms, efficient control signaling, and fast AP reassignment mechanisms. In this scenario, software defined networking (SDN) technology may be suitable for network monitoring, signaling, and control. We recently proposed embedding virtual switches in user terminals for direct management by an SDN controller, further contributing to SDN-oriented access network optimization. However, since users may restrict terminal-side traffic monitoring for privacy reasons (a common assumption by previous authors), we infer user traffic classes at the APs. On the other hand, since handovers will be more frequent in dense small-cell networks (e.g., mmWave-based 5G deployments will require dense network topologies with inter-site distances of ~150-200 m), the delay to take assignment decisions should be minimal. To this end, we propose taking fast decisions based exclusively on extremely simple network-side application flow-type predictions based on past user behavior. Using real data we show that a centralized allocation algorithm based on those predictions achie
This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity, and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and
In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% pred
Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm
The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude,
This article reveals an adequate comprehension of basic defense, security challenges, 2 and attack vectors in deploying multi-network slicing. Network slicing is a revolutionary concept 3 of providing mobile network on-demand and expanding mobile networking business and services 4 to a new era. The new business paradigm and service opportunities are encouraging vertical 5 industries to join and develop their own mobile network capabilities for enhanced performances 6 that are coherent with their applications. However, a number of security concerns are also raised 7 in this new era. In this article, we focus on the deployment of multi-network slicing with multi8 tenancy. We identify the security concerns, and discuss about the defense approaches such as 9 network slice isolation and insulation in a multi-layer network slicing security model. Also, we 10 identify the importance to appropriately select the network slice isolation points, and propose 11 a generic framework to optimize the isolation policy regarding the implementation cost while 12 guaranteeing the security and performance requirements.