scholarly journals Modeling Multi-Order Adaptive Processes by Self-Modeling Networks

2020 ◽  
Author(s):  
Jan Treur
Keyword(s):  
Modeling And Simulation ◽  
Network Structure ◽  
Software Environment ◽  
Network Nodes ◽  
Network Adaptation ◽  
Adaptive Processes ◽  
Base Network ◽  
Network Extension

A self-modeling network for some base network is a network extension that represents part of the base network structure by a self-model in terms of added network nodes and connections for them. By iterating this construction, multi-order network adaptation is easily obtained. A dedicated software environment for self-modeling networks that has been developed supports the modeling and simulation processes. This will be illustrated for a number of adaptation principles from a number of application domains.

scholarly journals Predicting partially observed processes on temporal networks by Dynamics-Aware Node Embeddings (DyANE)

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Koya Sato ◽  
Mizuki Oka ◽  
Alain Barrat ◽  
Ciro Cattuto
Keyword(s):  
Network Structure ◽  
Machine Learning Algorithms ◽  
Temporal Networks ◽  
Dynamical Processes ◽  
Network Nodes ◽  
Partially Observed ◽  
Low Dimensional ◽  
Vector Representations ◽  
Infectious Disease Dynamics ◽  
Random Times

AbstractLow-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks – rather than of the network structure itself – with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a lossless modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes’ epidemic states in single instances of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.

Comparison of the Network Structure and the Pattern of Network Extension in Intracellular Organelles of Yeast

2011 ◽  
Vol 58 (2) ◽  
pp. 372-376 ◽  
Author(s):  
Yoon Kyeong Lee ◽  
Jaeul Ku ◽  
Jea Woon Ryu ◽  
Hak Yong Kim ◽  
Myung Ho Yeo ◽  
...  
Keyword(s):  
Network Structure ◽  
Intracellular Organelles ◽  
Network Extension

Modeling higher order adaptivity of a network by multilevel network reification

2020 ◽  
Vol 8 (S1) ◽  
pp. S110-S144 ◽  
Author(s):  
Jan Treur
Keyword(s):  
Network Architecture ◽  
Network Models ◽  
Higher Order ◽  
Second Order ◽  
Adaptation Level ◽  
Network Adaptation ◽  
Base Level ◽  
Generic Architecture ◽  
Adaptation Processes ◽  
Base Network

AbstractIn network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.

Dynamic Modeling and Simulation of a 4-Wheel Integral Steering Vehicle

2015 ◽  
Vol 811 ◽  
pp. 284-290
Author(s):  
Cătălin Alexandru
Keyword(s):  
Dynamic Analysis ◽  
Modeling And Simulation ◽  
Dynamic Modeling ◽  
Virtual Prototype ◽  
Steering System ◽  
Software Environment ◽  
Steering Law

The work deals with the dynamic modeling and simulation of a 4-wheel steering vehicle. The steering system for the front wheels is a classical one (with pinion & rack), while for the rear wheels, a new design with rotational cam & translational follower has been developed by considering the integral steering law. The virtual prototype of the vehicle was modeled - simulated by using the MBS software environment ADAMS of MSC. The results of the dynamic analysis prove the performance of the proposed solution, in terms of handling and stability.

scholarly journals Modeling Complex Networks Based on Deep Reinforcement Learning

2022 ◽  
Vol 9 ◽  
Author(s):  
Wenbo Song ◽  
Wei Sheng ◽  
Dong Li ◽  
Chong Wu ◽  
Jun Ma
Keyword(s):  
Reinforcement Learning ◽  
Complex Networks ◽  
Network Topology ◽  
Network Structure ◽  
Dynamic Change ◽  
Intelligent Agent ◽  
Small World ◽  
Network Nodes ◽  
Scale Free ◽  
Internal Mechanism

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

scholarly journals Method of ensuring the survivability of highly mobile computer networks

2021 ◽  
Vol 5 (2) ◽  
pp. 159-165
Author(s):  
Vitalii Tkachov ◽  
Andriy Kovalenko ◽  
Heorhii Kuchuk ◽  
Iana Ni
Keyword(s):  
Computer Networks ◽  
Network Structure ◽  
Critical Values ◽  
Theoretical Research ◽  
Main Function ◽  
Local Damage ◽  
Mobile Computer ◽  
Network Nodes ◽  
Low Level ◽  
Local Destruction

The article discusses the features of the functioning of mobile computer networks based on small-sized aircraft (highly mobile computer networks). It is shown that such networks, in contrast to stationary or low-mobile ones, have a low level of survivability in case of local damage to their nodes. The purpose of the article is to develop a method for ensuring the survivability of highly mobile computer networks under conditions of destructive external influences, which leads to local destruction of network nodes or links between them, using the method of assessing survivability at all stages of network functioning, by changing the main function to implement all available strategies for the functioning of the network when determining the critical values of the integrity of the network and its ability to perform at least one of the available functions. The results obtained allow: to continue the development of theoretical research in the development of strategies for managing highly mobile computer networks in extreme situations; to develop an applied solution to ensure the survivability of highly mobile computer networks by building multifunctional or redundant structures, increasing the value of their redundancy. The studies allow us to conclude that the proposed method can be used at the design stages of highly mobile computer networks, characterized by increased survivability and capable of functioning in conditions of multiple local damages without catastrophic destructive consequences for the network structure.

scholarly journals AGENT-BASED SIMULATION OF DDOS ATTACKS AND DEFENSE MECHANISMS

2014 ◽  
pp. 113-123
Author(s):  
Igor Kotenko ◽  
Alexander Ulanov
Keyword(s):  
Modeling And Simulation ◽  
Defense Mechanisms ◽  
Denial Of Service ◽  
Software Agents ◽  
Ddos Attacks ◽  
Software Environment ◽  
Agent Based ◽  
Agent Simulation ◽  
Multi Agent ◽  
Main Components

The paper considers an approach to modeling and simulation of cyber-wars in Internet between the teams of software agents. According to this approach, the cybernetic opposition of malefactors and security systems is represented by the interaction of two different teams of software agents – malefactors’ team and defense team. The approach is considered by an example of modeling and simulation of “Distributed Denial of Service” (DDoS) attacks and protection against them. The paper also describes the software environment for multi-agent simulation of defense mechanisms against DDoS attacks developed by the authors and different experiments. The main components of the software environment are outlined. One of the numerous experiments on protection against DDoS attacks is described in detail. The environment developed is based OMNeT++ INET Framework.

scholarly journals User Profile Preserving Social Network Embedding

2017 ◽  
Author(s):  
Daokun Zhang ◽  
Jie Yin ◽  
Xingquan Zhu ◽  
Chengqi Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Dimensional Space ◽  
User Profile ◽  
Network Embedding ◽  
Network Nodes ◽  
Profile Information ◽  
Low Dimensional ◽  
Performance Gains

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.

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