scholarly journals Compacting oblivious agents on dynamic rings

2021 ◽  
Vol 7 ◽  
pp. e466
Author(s):  
Shantanu Das ◽  
Giuseppe Antonio Di Luna ◽  
Daniele Mazzei ◽  
Giuseppe Prencipe
Keyword(s):  
Necessary Conditions ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Continuous Part ◽  
Heterogenous Agents ◽  
Common Task ◽  
Persistent Memory ◽  
Colored Version ◽  
Group Agents ◽  
Configuration Problem

In this paper we investigate dynamic networks populated by autonomous mobile agents. Dynamic networks are networks whose topology can change continuously, at unpredictable locations and at unpredictable times. These changes are not considered to be faults, but rather an integral part of the nature of the system. The agents can autonomously move on the network, with the goal of solving cooperatively an assigned common task. Here, we focus on a specific network: the unoriented ring. More specifically, we study 1-interval connected dynamic rings (i.e., at any time, at most one of the edges might be missing). The agents move according to the widely used Look–Compute–Move life cycle, and can be homogenous (thus identical) or heterogenous (agents are assigned colors from a set of c > 1 colors). For identical agents, their goal is to form a compact segment, where agents occupy a continuous part of the ring and no two agents occupy the same node: we call this the Compact Configuration Problem. In the case of agents with colors, called the Colored Compact Configuration Problem, the goal is to group agents such that each group is formed by all agents having the same color, it occupies a continuous segment of the network, and groups of agents having different colors occupy distinct areas of the network. In this paper we determine the necessary conditions to solve both proposed problems. For all solvable cases, we provide algorithms for both the monochromatic and the colored version of the compact configuration problem. All our algorithms work even for the simplest model where agents have no persistent memory, no communication capabilities and do not agree on a common orientation within the network. To the best of our knowledge this is the first work on the compaction problem in a dynamic network.

scholarly journals DNA-based constitutional dynamic networks as functional modules for logic gates and computing circuit operations

2021 ◽  
Vol 12 (15) ◽  
pp. 5473-5483
Author(s):  
Zhixin Zhou ◽  
Jianbang Wang ◽  
R. D. Levine ◽  
Francoise Remacle ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Logic Gates ◽  
Functional Modules ◽  
Logic Operations

A nucleic acid-based constitutional dynamic network (CDN) provides a single functional computational module for diverse input-guided logic operations and computing circuits.

An experimental evaluation and guideline for path finding in weighted dynamic network

2021 ◽  
Vol 14 (11) ◽  
pp. 2127-2140
Author(s):  
Mengxuan Zhang ◽  
Lei Li ◽  
Xiaofang Zhou
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Real Life ◽  
Dynamic Network ◽  
Problem Space ◽  
Network Applications ◽  
Complete Problem ◽  
Path Computation ◽  
Shortest Path Computation

Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the Dynamic Shortest Path (DSP) problem has drawn lots of attention in recent years. However, as DSP has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the determinant dimensions and constraint dimensions of the DSP problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art DSP methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of dynamic degree to classify the dynamic environments and use throughput to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.

Data Mining Techniques for Communities’ Detection in Dynamic Social Networks

Data Mining ◽  
2013 ◽  
pp. 719-733
Author(s):  
Céline Robardet
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Added Value ◽  
Strong Interactions ◽  
Time Stamp ◽  
Efficient Computation ◽  
Real World Data ◽  
Positive Side ◽  
Dynamic Social Networks ◽  
Temporal Events

Social network analysis studies relationships between individuals and aims at identifying interesting substructures such as communities. This type of network structure is intuitively defined as a subset of nodes more densely linked, when compared with the rest of the network. Such dense subgraphs gather individuals sharing similar property depending on the type of relation encoded in the graph. In this chapter we tackle the problem of identifying communities in dynamic networks where relationships among entities evolve over time. Meaningful patterns in such structured data must capture the strong interactions between individuals but also their temporal relationships. We propose a pattern discovery method to identify evolving patterns defined by constraints. In this paradigm, constraints are parameterized by the user to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. In the proposed approach, dense and isolated subgraphs, defined by two user-parameterized constraints, are first computed in the dynamic network restricted at a given time stamp. Second, the temporal evolution of such patterns is captured by associating a temporal event types to each subgraph. We consider five basic temporal events: the formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next one. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible thanks to efficient pruning patterns strategies. Experimental results on real-world data confirm the practical feasibility of our approach. We evaluate the added-value of the method, both in terms of the relevancy of the extracted evolving patterns and in terms of scalability, on two dynamic sensor networks and on a dynamic mobility network.

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Community Structures ◽  
Block Model ◽  
Real World Data ◽  
Stochastic Block Model ◽  
Dynamic Social Networks ◽  
Influential Nodes

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

scholarly journals The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 212
Author(s):  
Zhiwei Yang ◽  
Weigang Wu
Keyword(s):  
Network Topology ◽  
Information Dissemination ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Models ◽  
Point Of View ◽  
Path Model ◽  
Dynamic Network Models ◽  
Topology Changes ◽  
Practical Design

A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

scholarly journals Dynamic network prediction

2020 ◽  
Vol 8 (4) ◽  
pp. 574-595
Author(s):  
Ravi Goyal ◽  
Victor De Gruttola
Keyword(s):  
Social Relationships ◽  
Network Structure ◽  
Broad Class ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Simulation Studies ◽  
Population Composition ◽  
Statistical Framework ◽  
Network Properties ◽  
Network Prediction

AbstractWe present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network (DTEQT) using machine learning

2019 ◽  
Vol 18 (01) ◽  
pp. 1941020
Author(s):  
S. Ananth ◽  
A. M. Kalpana ◽  
R. Vijayarajeswari
Keyword(s):  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Wireless Sensor ◽  
Software Defined Network ◽  
Mobile Nodes ◽  
Energy Constrained

Wireless Sensor Network (WSN) is the interconnection between things or objects embedded with hardware and software. In WSN, small end devices (like sensors) and high end devices (like servers) are connected to the Internet. For WSN enabled in Software-Defined Network (SDN), the routers are controlled using a controller server node. It is a dynamic network due to the presence of mobile nodes and energy constrained nodes. The routing is the process of detecting route from source to target. In dynamic networks like WSN, routing is a challengeable task. This paper is to provide a routing solution for backboneless SDN-enabled WSN. The proposed work enhances routing Quality of Service (QoS) in WSN. The paths are dynamically reallocated to reduce the packet loss.

scholarly journals Dynamic network partnerships and social contagion drive cooperation

2018 ◽  
Vol 285 (1893) ◽  
pp. 20181973 ◽  
Author(s):  
Roslyn Dakin ◽  
T. Brandt Ryder
Keyword(s):  
Repeated Measures ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Social Contagion ◽  
Evolution Of Cooperation ◽  
Cooperative Behaviour ◽  
Data Logging ◽  
Sources Of Variation ◽  
Positive Correlations ◽  
Multiple Membership

Both reciprocity and positive assortment (like with like) are predicted to promote the evolution of cooperation, yet how partners influence each other's behaviour within dynamic networks is not well understood. One way to test this question is to partition phenotypic variation into differences among individuals in the expression of cooperative behaviour (the ‘direct effect’), and plasticity within individuals in response to the social environment (the ‘indirect effect’). A positive correlation between these two sources of variation, such that more cooperative individuals elicit others to cooperate, is predicted to facilitate social contagion and selection on cooperative behaviour. Testing this hypothesis is challenging, however, because it requires repeated measures of behaviour across a dynamic social landscape. Here, we use an automated data-logging system to quantify the behaviour of 179 wire-tailed manakins, birds that form cooperative male–male coalitions, and we use multiple-membership models to test the hypothesis that dynamic network partnerships shape within-individual variation in cooperative behaviour. Our results show strong positive correlations between a bird's own sociality and his estimated effect on his partners, consistent with the hypothesis that cooperation begets cooperation. These findings support the hypothesis that social contagion can facilitate selection for cooperative behaviour within social networks.

scholarly journals Modelling approaches for simple dynamic networks and applications to disease transmission models

2012 ◽  
Vol 468 (2141) ◽  
pp. 1332-1355 ◽  
Author(s):  
Istvan Z. Kiss ◽  
Luc Berthouze ◽  
Timothy J. Taylor ◽  
Péter L. Simon
Keyword(s):  
Steady State ◽  
Network Model ◽  
Disease Transmission ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Link Type ◽  
Dynamic Network Model ◽  
Pairwise Model ◽  
Closure Relations ◽  
Transmission Models

In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.

scholarly journals Fuzzy-based Adaptive Multipath for En-route Filtering in Dynamic Wireless Sensor Network

2019 ◽  
Vol 8 (4) ◽  
pp. 12377-12385
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Dynamic Networks ◽  
Dynamic Environment ◽  
Dynamic Network ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Dos Attacks ◽  
Fixed Path

Wireless sensor network is formed with limited energy resources, easily compromised by an adversary because of hostile environments. Adversary may use compromised nodes to inject false reports and launch DoS attacks, thus, sensor nodes are prone to failure and which makes the network topology configurations highly dynamic in real world applications. A variety of en-route filtering schemes have been proposed to drop and defeat these attacks by using their own cryptographic methods. Some of them ask for a fixed path between a base station and each cluster, so they are not feasible for dynamic network. Additionally, other proposals do not consider various environmental variables in a dynamic environment, so they only choose static paths. In contrast, we consider topology changes, communication costs, the maximum number of key dissemination hops, and the spread of nodes for providing optimum filtering capacity. This paper presents a fuzzy-based adaptive multipath selection method in dynamic environment of a wireless sensor network. Our proposed method can adjust the optimized number of multipaths during key dissemination. Experimental results show that relatively higher filtering capacity with lower energy consumption and suitable nodes for highly dynamic networks.

Sign in / Sign up

Export Citation Format

Share Document