Synchronization and Graph Topology

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Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Aim: Adolescents born very preterm (VPT;

Dynamical flow networks serve as macroscopic models for, e.g., transportation networks, queuing networks, and distribution networks. While the flow dynamics in such networks follow the conservation of mass on the links, the outflow from each link is often ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

Population equations for infinitely large networks of spiking neurons have a long tradition in theoret-ical neuroscience. In this work, we analyze a recent generalization of these equations to populations of finite size, which takes the form of a nonlinear ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

Network alignment is the task of identifying topologically and semantically similar nodes across (two) different networks. It plays an important role in various applications ranging from social network analysis to bioinformatic network interactions. Howeve ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Cycles are one of the fundamental subgraph patterns and being able to enumerate them in graphs enables important applications in a wide variety of fields, including finance, biology, chemistry, and network science. However, to enable cycle enumeration in r ...

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies rema ...

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in ...

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

Automating experimental procedures has resulted in an unprecedented increase in the volume of generated data, which, in turn, has caused an accumulation of unprocessed data. As a result, the need to develop tools to analyze data systematically has been ris ...

Synaptic plasticity underlies our ability to learn and adapt to the constantly changing environment. The phenomenon of synapses changing their efficacy in an activity-dependent manner is often studied in small groups of neurons in vitro or indirectly throu ...

In engineering, oscillatory instabilities and resonances are often considered undesirable flow features and measures are taken to avoid them. This may include avoiding certain parametric regions or implementing control and mitigation strategies. However, t ...

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

Synchronization and Graph Topology

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Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Network-based kinetic models: Emergence of a statistical description of the graph topology

Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents

The Strong Integral Input-to-State Stability Property in Dynamical Flow Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Equilibria in Network Constrained Energy Markets

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

On a Finite-Size Neuronal Population Equation

Discovering Influencers in Opinion Formation Over Social Graphs

Network Alignment With Holistic Embeddings

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-constrained Cycles

Cortical cell assemblies and their underlying connectivity: an in silico study

Bayesian reconstruction of memories stored in neural networks from their connectivity

Privacy-Preserving Machine Learning on Graphs

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Modeling, analysis, and design of complex biological networks

Principles of Network Plasticity in Neocortical Microcircuits

Self-sustained dynamics and forced resonant oscillations in flows: cross-junction jets and sloshing liquids

Global, Passive Detection of Connection Tampering