Dynamics on Networks

Gulati, Nohria, and Zaheer (2000) aim to introduces the important role of networks of interfirm ties, by adopting a network lens to explain differential firm profitability changes from an atomistic to a relational model—and therein lies its true power. They identify five potential key areas of strategy research for incorporating strategic networks: (1) industry structure, (2) positioning within an industry, (3) inimitable firm resources and capabilities, (4) contracting and coordination costs, and (5) dynamic network constraints and benefits. For each of these issues, the paper outlines some important insights that result from considering the role of strategic networks.

The social network analysis (SNA) is a promising instrument in medievistic research. In a weakly institutionalised face-to-face society concrete social relations and interactions were obviously very important. Particularly, historians are interested in the problem of network dynamics, because historical processes can be understood as the development of networks in time. One of the most important factors, which influences a given network, are conflicts. We can model mathematically their emergent effects using Fritz Heiders concept of the so called structural balance. The paper shortly introduces the monograph „The Empire as a network of princes“, which uses SNA techniques and structural balance for modelling and explaining a large 13th-century political network.

U.S. companies in the eighties have been forced to rethink competitive approaches. Business landscape was describe as patterns of technological change, international trade, unpredictable competitors. Every industry requires the presence of Prospectors, Defenders, and Analyzers. Prospectors generate the technological and product innovations that push the industry forward. Analyzers rationalize some of these innovations for marketability and ease of manufacture, and Defenders lower costs to the minimum in certain product areas to facilitate mass consumption. Miles and Snow (1986) predicted that Defender companies would choose to perform the producer role, and Prospectors would select the designer role.

This paper addresses the question of how communication structures arise in high and low performing teams. It draws on different social theories to predict typical patterns that correlate with high or low team performance. This theoretical approach is evaluated using data from virtual teams in an experimental design. We show that a high level of reciprocity, a high in-degree for communication recipients and direct communication between the team members (rather than communication via intermediaries) are indicators of high team performance. The dynamic network structures which have been established and developed by the exchange of several hundred e-mails are analyzed using a new dynamic, actor-oriented event model for social networks.

Battlefield theater applications require supporting large number of nodes. It can facilitate many multi-hop paths between each source and destination pairs. For scalability, it is critical that for supporting network centric applications with large set of nodes require hierarchical approach to designing networks. In this research we consider using Mobile Ad Hoc Network (MANET) with multiple clusters. Each cluster supports a few nodes with a cluster head. The intra-cluster connectivity amongst the nodes within the cluster is supported by multi-hop connectivity to ensure handling mobility in such a way that no service disruption can occur. The inter-cluster connectivity is also achieved by multi-hop connectivity. However, for inter-cluster communications, only cluster heads are connected. The selection of intra-cluster
communications and inter-cluster communications allow scalability of the network to support multiservices
applications end-to-end with a desired Quality of Service (QoS). This paper proposes graph theoretic approach to establish efficient connection between a source and a destination within each cluster in intra-cluster network and between clusters in inter-cluster network. Graph theoretic approach traditionally was applied networks where nodes are static or fixed. In this paper, we have applied the
graph theoretic routing to MANET where nodes are mobile. One of the important challenges in MANET is to support an efficient routing algorithm for multi-hop communications across many nodes which are dynamic in nature. However, dynamic behavior of the nodes requires greater understanding of the node degree and mobility at each instance of time in order to maintain end-to-end QoS for multi-service provisioning. This paper demonstrates graph theoretic approach produces an optimum multi-hop
connectivity path based on cumulative minimum degree that minimizes the contention and scheduling delay end-to-end. It is applied to both intra-cluster communications as well as inter-cluster communications. The performance shows that having a multi-hop connectivity for intra-cluster communications is more power efficient compared to broadcast of information with maximum power coverage. Each cluster performs similarly and the algorithm is also used for inter-cluster communications.Our simulation results show that the proposed graph theoretic routing approach will reduce the overall delay and improves the physical layer data frame transmission.

Since organizations are dynamic systems, they change over time. Communication within organizations represents these changes. The use of social network analysis is an established (although still not common) approach to better understanding organizational structures. Methods and business applications for dynamic analyses are still a new, though promising field. The increasing use of computer mediated communication, like email, provides particularly relevant data for studying organizations. Focusing exclusively on the changes in the use of email communication over time, two different methodological approaches are used to analyze the email data stream: descriptive network visualizations and a new model using exponential random graph models for event stream data. In a simulation with 97 students, a merger of two different organizations was replicated.

We investigated the mutually supporting role of chaotic activity and evolving structure in a complex network. An initially randomly coupled network with chaotic activation is adaptively rewired according to dynamic coherence between its units. The evolving network reaches a small-world structure. Meanwhile, collective network activity tends to an intermittent dynamic clustering regime. Spontaneous chaotic activity and adaptively evolving structure jointly enhance signal propagation capacity.

The dynamic communication network within teams affects the performance of teams. But how can we analyze these emerging networks? We identified three research areas that have to be included for this purpose. First we summarize empirical studies concerning team networks and performance to point out the need of longitudinal investigations. Second we present the multi-level multi-theoretical model by Monge and Contractor (2003) which provides a theoretical framework to explain the evolution of communication networks within teams. Third a stochastic model is introduced that allows analyzing event based data, like e-mail streams, using exponential random graph models. We propose to include these three research areas that enable researchers and practitioners to analyze dynamic network patterns of virtual teams.

Spontaneous brain activity is organized into resting-state networks (RSNs) involved in internally-guided, higher-order mental functions (default mode, central executive and salience networks) and externally-driven, specialized sensory and motor processing (auditory, visual and sensorimotor networks). RSNs are characterized by their functional connectivity in terms of within-network cohesion and between-network integration, and by their dynamic properties in terms of synchrony and metastability. We examined the relationship between functional connectivity and dynamic network features using fMRI data and an anatomically constrained Kuramoto model. Extrapolating from simulated data, synchrony and metastability across the RSNs emerged at coupling strengths of 5 ≤ k ≤ 12. In the empirical RSNs, higher metastability and synchrony were respectively associated with greater cohesion and lower integration. Consistent with their dual role in supporting both sustained and diverse mental operati...

U.S. companies in the eighties have been forced to rethink competitive approaches. Business landscape was describe as patterns of technological change, international trade, unpredictable competitors. Every industry requires the presence of Prospectors, Defenders, and Analyzers. Prospectors generate the technological and product innovations that push the industry forward. Analyzers rationalize some of these innovations for marketability and ease of manufacture, and Defenders lower costs to the minimum in certain product areas to facilitate mass consumption. Miles and Snow (1986) predicted that Defender companies would choose to perform the producer role, and Prospectors would select the designer role.

The political system of the medieval German empire can be understood as a network of princely and comital actors which acted under the conditions of structural balance (Fritz Heider). The „Empire as a network of princes“ constituted a complex and interdependent system which was formed by numerous interactions of positive or negative character like kinship, political alliances, personal contacts (e.g. document witness), territorial or status competition, legal and military conflicts and so on. This political environment also determinated the royal activities: Frictions and conflicts between the princes must have a negative impact on the relation of both Staufian sovereigns. The presentation gives a short overview of the content of my monograph “Das Reich als Netzwerk der Fürsten” (2013). It illustrates some of its theoretical and methodological aspects, especially the principles of structural balance and the structure of the database. Various sociograms of the “network of princes” show the dynamical development of German politics between 1225 and 1235.

First-order network flow models are coupled systems of differential equations which describe the build-up and dissipation of congestion along network road segments, known as link models. Models describing flows across network junctions, referred to as node models, play the role of the coupling between the link models and are responsible for capturing the propagation of traffic dynamics through the network. Node models are typically stated as optimization problems, so that the coupling between the link dynamics is not known explicitly. This renders network flow models analytically intractable. This paper examines the properties of node models for urban networks. Solutions to node models that are free of traffic holding, referred to as holding-free solutions , are formally defined and it is shown that flow maximization is only a sufficient condition for holding-free solutions. A simple greedy algorithm is shown to produce holding-free solutions while also respecting the invariance principle. Staging movements through nodes in a manner that prevents conflicting flows from proceeding through the nodes simultaneously is shown to simplify the node models considerably and promote unique solutions. The staging also models intersection capacities in a more realistic way by preventing unrealistically large flows when there is ample supply in the downstream and preventing artificial blocking when some of the downstream supplies are restricted.

Spontaneous brain activity is organized into resting-state networks (RSNs) involved in internally-guided, higher-order mental functions (default mode, central executive and salience networks) and externally-driven, specialized sensory and motor processing (auditory, visual and sensorimotor networks). RSNs are characterized by their functional connectivity in terms of within-network cohesion and between-network integration, and by their dynamic properties in terms of synchrony and metastability. We examined the relationship between functional connectivity and dynamic network features using fMRI data and an anatomically constrained Kuramoto model. Extrapolating from simulated data, synchrony and metastability across the RSNs emerged at coupling strengths of 5 ≤ k ≤ 12. In the empirical RSNs, higher metastability and synchrony were respectively associated with greater cohesion and lower integration. Consistent with their dual role in supporting both sustained and diverse mental operations, higher-order RSNs had lower metastability and synchrony. Sensory and motor RSNs showed greater cohesion and metastability, likely to respectively reflect their functional specialization and their greater capacity for altering network states in response to multiple and diverse external demands. Our findings suggest that functional and dynamic RSN properties are closely linked and expand our understanding of the neural architectures that support optimal brain function. Brain activity at rest is organized into functional resting-state networks (RSNs) defined by their spatiotemporal configuration and functional roles 1–4. The default mode network (DMN) 5 , the central executive network (CEN) 6 and the salience network (SAL) 7 support diverse and mostly internally-guided processes. The DMN is implicated in self-referential and integrative processes 5 , the CEN is involved in goal-directed selection of stimuli and responses 6 , and the SAL processes information relating to varied forms of cognitive, affective and homeostatic salience 7. Further networks, particularly the auditory (AN), visual (VN) and sensorimotor (SMN) networks, are known to support more specialized and mostly externally-driven functions 8. Using functional connectivity metrics, it is possible to characterize RSNs based on their internal cohesion and integration. RSNs with high within-network connectivity can be considered cohesive while those with low within-network connectivity can be considered incohesive 9. Similarly, RSNs with high between-network con-nectivity can be designated as connector networks while those with low between-network connectivity can be designated as provincial networks 9. In the adult brain, the DMN has a unique role as a cohesive connector, the CEN and SAL behave as incohesive connectors while the AN, VN and SMN function as cohesive provincial networks 9. These functional properties are considered essential components of the optimal functional configuration of the human brain. More recently, the functional behavior of brain networks has been considered in terms of their synchrony and metastability that attempt to capture dynamic changes in network configuration. Synchrony is a measure of the synchronization of the individual network regions while metastability refers to the degree of variability in network states over time 10,11. Optimal brain function is thought to occur at levels of synchrony and metastability that allow both efficient information flow and the adaptive changes to network states in response to external or internal demands 10,12–18. Previous studies have found that synchrony and metastability differ between RSNs 12,13,19. However, it is unclear whether such differences are associated with the functional connectivity profiles of the RSNs. The aim of the current paper is to test whether the functional and dynamic properties of RSNs are indeed linked. Specifically, we hypothesized that the role of internally-guided RSNs as connector networks would be associated with lower metastability and synchrony; lower metastability would enable connector networks to

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Abstract:
The political system of the medieval German empire can be understood as a network of princely and comital actors which acted under the conditions of Fritz Heider´s theory of structural balance. The „Empire as a network of princes“ constituted a complex and interdependent system which was formed by numerous interactions of positive or negative character like kinship, political alliances, personal contacts (e.g. document witness), territorial or status competition, legal and military conflicts and so on. This political environment also determinated the royal activities: Frictions and conflicts between the princes must have a negative impact on the relation of both Staufian sovereigns. With the help of network-analytical methods the monograph analyses the political system of Germany during a period of ten years and gives new answers to some long-discussed questions: Did Henry VII. really offended the princes?  Why did he lose the support of the German princes in such a dramatical way?

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

Coupled Map Lattices (CML) offer a new framework for modelling visual information processes. The framework involves computing with nonstationary patterns of synchronized activity. In this framework structural features of the visual field emerge through the lateral interaction of locally coupled non-linear maps. Invariant representations develop independent of top-down, or re-entrant feedback. These representations distort certain features of the pattern, giving rise to visual field illusions. Boundary contours, among others, are emphasized, which suggests that special cases of boundary-contour problem could be solved by the system. Simulation studies were performed to test the hypothesis that the system represents visual patterns in a solid/outline invariant manner. A standard back-propagation neural network trained with a CML-filtered set of solid images and tested with CML-filtered outline versions of the same
set of images (or vice versa) showed perfect generalization. Generalization failed to occur for unfiltered or contour-filtered images. The CML-representations, therefore, were concluded to be solid/outline invariant.

In this work we review a class of deterministic nonlinear models for the propagation of infectious diseases over contact networks with strongly-connected topologies. We consider network models for susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) settings. In each setting, we provide a comprehensive nonlinear analysis of equilibria, stability properties, convergence, monotonicity, positivity, and threshold conditions. For the network SI setting, specific contributions include establishing its equilibria, stability, and positivity properties. For the network SIS setting, we review a well-known deterministic model, provide novel results on the computation and characterization of the endemic state (when the system is above the epidemic threshold), and present alternative proofs for some of its properties. Finally, for the network SIR setting, we propose novel results for transient behavior, threshold conditions, stability proper...

In this paper, we propose a new traffic flow model of the Long Term Evaluation (LTE) network for the Evolved Universal Terrestrial Radio Access Network (E-UTRAN). Here only one Evolve Node B (eNB) nearest to the Mobility Management Entity (MME) and Serving Gateway (S-GW) will use the S1 link to bridge the E-UTRAN and Evolved Packet Core (EPC). All the eNBs of a tracking area will be connected to each other by the X2 link. Determination of capacity of a links of such a network is a challenging job since each node offers its own traffic and at the same time conveys traffic of other nodes. In this paper, we apply maximum flow algorithm including superposition theorem to solve the traffic flow of radio network. Using the total flow per subcarrier, a new traffic model is also developed in the paper. The relation among the traffic parameters: 'blocking probability', 'offered traffic', 'instantaneous capacity', 'average holding time', and 'number of users' are shown graphically under both QPSK and 16-QAM. The concept of the network will be helpful to improve the SINR of the received signal ofeNBslocated long distance relative to MME/S-GW.

Coupled Maps (CM) offer a new approach towards modeling the visual system. Perceptual representations are understood in this framework as transient, non-stationary patterns of activity. They enable the detection of local and non-local features, the integration of local features in global representations, as well as fast switching between alternative global representations. Adaptive versions of the CM provide a particularly powerful tool for modeling the dynamic interaction of the visual system and memory. The systems generate a number of classical memory phenomena, such as iconic storage of patterns, short and medium storage autonomous decay, long term storage and spontaneous re-instantiation. Unlike other models, which often show catastrophic interference and forgetting, automatic separation between patterns occurs as a result of divergent tendencies in chaotic dynamics. Instead of from design, these features emerge from the self-organizing dynamics of the system and, for this reason, CM are argued to naturally implement the dynamics of perception and memory.

In this paper we propose a class of propagation models for multiple competing products over a social network. We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors. A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products. According to the chronological order of social and self conversions, we develop two Markov-chain models and, based on the independence approximation, we approximate them with two respective difference equations systems. Theoretical analysis on these two approximation models reveals the dependency of the systems' asymptotic behavior on the structures of both the product-conversion graph and the social network, as well as the initial condition. In addition to the theoretical work, accuracy of the independence approximation and the asymptotic behavior of the Markov-chain model are investigated via numerical analysis, f...

Methods for detecting and summarizing emergent keywords have been extensively studied since social media and microblogging activities have started to play an important role in data analysis and decision making. We present a system for monitoring emergent keywords and summarizing a document stream based on the dynamic semantic graphs of streaming documents. We introduce the notion of dynamic eigenvector centrality for ranking emergent keywords, and present an algorithm for summarizing emergent events that is based on the minimum weight set cover. We demonstrate our system with an analysis of streaming Twitter data related to public security events.

We study the problem of the emergence of cooperation
in dynamic signed networks where agent strategies coevolve
with relational signs and network topology. Running simulations  based on an agent-based model, we compare results obtained in a regular lattice initialization with those obtained on a comparable random network initialization. We show that the increased degree heterogeneity at the outset enlarges the parametric conditions in which cooperation survives in the long run. Furthermore, we show how the presence of sign-dependent emotional strategies catalyze the evolution of cooperation with both network topology initializations.

Spontaneous activity in biological neural networks shows patterns of dynamic synchronization. We propose that these patterns support the formation of a small-world structure—network connectivity optimal for distributed information processing. We present numerical simulations with connected
Hindmarsh–Rose neurons in which, starting from random connection distributions, small-world networks evolve as a result of applying an adaptive rewiring rule. The rule connects pairs of neurons that tend fire in synchrony, and disconnects ones that fail to synchronize. Repeated application of the rule leads to small world structures. This mechanism is robustly observed
for bursting and irregular firing regimes.

9th International Conference on Cybernetics & Informatics (CYBI 2022) is a forum for presenting new advances and research results in the fields of information, control and system theory, understands the design and function of any system and the relationship among these applications. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. This conference aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.

Network resources allocated for particular application traffic are aware of the characteristics of L4+ content to be transmitted. One embodiment of the invention realizes network resource allocation in terms of three intelligent modules, gateway, provisioning and classification. A gateway module exerts network control functions in response to application requests for network resources. The network control functions include traffic path setup, bandwidth allocation and so on. Characteristics of the content are also specified in the received application network resource requests. Under request of the gateway module, a provisioning module allocates network resources such as bandwidth in optical networks and edge devices as well. An optical network resource allocation leads to a provisioning optical route. Under request of the gateway module, a classification module differentiates applications traffic according to content specifications, and thus creates and applies content-aware rule data for edge devices to forward content-specified traffic towards respective provisioning optical routes.

—In this paper we propose a class of propagation models for multiple competing products over a social network. We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors. A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products. According to the chronological order of social and self conversions, we develop two Markov-chain models and, based on the independence approximation, we approximate them with two corresponding difference equations systems. Our theoretical analysis on these two approximated models reveals the dependency of their asymptotic behavior on the structures of both the product-conversion graph and the social network, as well as the initial condition. In addition to the theoretical work, we investigate via numerical analysis the accuracy of the independence approximation and the asymptotic behavior of the Markov-chain model, for the case where social conversion occurs before self conversion. Finally, we propose two classes of games based on the competitive propagation model: the one-shot game and the dynamic infinite-horizon game. We characterize the quality-seeding trade-off for the first game and the Nash equilibrium in both games.

Network coding has been shown to achieve optimal multicast throughput, yet at an expensive computation cost: every node in the network has to code. Interested in minimizing resource consumption of network coding while maintaining its performance, in this paper, we propose a practical network coder placement algorithm which achieves comparable content distribution time as network coding, and at the same time, substantially reduces the number of network coders compared to a full network coding solution in which all peers have to encode, i.e. become encoders. Our algorithm is derived from two key elements. First, it is based on the insight that coding at upstream peers eliminates information duplication to downstream peers, which results in efficient content distribution. Second, our placement strategy exploits centrality characteristics of the network topology to quickly determine key positions to place encoders. Performance evaluation using various topology and algorithm parameters confirms the effectiveness of our proposed method.

Results from two studies on longitudinal friendship networks are presented, exploring the impact of a gratitude intervention on positive and negative affect dynamics in a social network. The gratitude intervention had been previously shown to increase positive affect and decrease negative affect in an individual but dynamic group effects have not been considered. In the first study, the intervention was administered to the whole network. In the second study, two social networks are considered and in each only a subset of individuals, initially low/high in negative affect respectively received the intervention as “agents of change”. Data was analyzed using stochastic actor-based modeling techniques to identify resulting network changes, impact on positive and negative affect and potential contagion of mood within the group. The first study found a group level increase in positive and a decrease in negative affect. Homophily was detected with
regard to positive and negative affect but no evidence of contagion was found. The network itself became more volatile along with a fall in rate of change of negative affect. Centrality measures indicated that the best broadcasters were the individuals with the least negative affect levels at the beginning of the study. In the second study, the positive and negative affect levels for the whole group depended on the initial levels of negative affect of the intervention recipients. There was evidence of positive affect contagion in the group where intervention recipients had low initial level of negative affect and contagion in negative affect for the group where recipients had initially high level of negative affect.

There are many studies of the conditions in which social contagion affects adoption processes in the marketing and diffusion literature. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infective Susceptible (SIS) model to assess the role of social contagion in adoption processes, accounting for changes in social dynamics over time. To study the innovation adoption dynamics over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also accounts for social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns; their effect may be overestimated if marketers fail to acknowledge the variations in social interactions.

Until recently, the transition from the Bronze Age to the Iron Age (ca. 1250–1000 BC) was viewed as a major cultural breaking point in the archaeology of the Aegean. The period before the destruction of the palaces around 1200 BC was considered to have been a golden age, whereas the period following the destructions – known as LH IIIC or the Postpalatial period – was treated as an era of general decline and deterioration. Recent research, however, shows that the impact of the destructions greatly varies between regions; many sites continued to be inhabited and were still actively engaged in overseas contacts. Particularly conspicuous in this respect are the connections between the Aegean and Italy, because they do not only continue but actually seem to grow stronger after the destructions. This situation cannot be adequately explained by current interpretative models that attribute a central role to the palaces in the organization of external relations.  As an alternative, I explore the potential of a network approach . One promising model is the so-called scale-free network. In this type of net¬work, some nodes (“hubs”) show a high degree of connectivity, whereas most nodes are connected to only a few others. As a result, the network is both robust and fragile: one failing hub will not cause a loss of connectedness, but the network will disinte¬grate when more major hubs fail. By using the scale-free network model, I arrive at the hypothesis that the survival of nonpalatial hubs was key to the continuation of Italo-Aegean interconnections. As a means to test this hypothesis, it is necessary to rephrase research questions in terms of network dynamics and to redirect the focus of research beyond the palaces by including other regions besides the archetypical palatial ones.

This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.

In this paper we propose a class of propagation models for multiple competing products over a social network. We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors. A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products. According to the chronological order of social and self conversions, we develop two Markov-chain models and, based on the independence approximation, we approximate them with two respective difference equations systems. Theoretical analysis on these two approximation models reveals the dependency of the systems' asymptotic behavior on the structures of both the product-conversion graph and the social network, as well as the initial condition. In addition to the theoretical work, accuracy of the independence approximation and the asymptotic behavior of the Markov-chain model are investigated via numerical analysis, f...

The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number of users, applications and services that are highly distributed with distinct and heterogeneous communications requirements. Resource allocation in this context requires dynamic, efficient and customized solutions and Bandwidth Allocation Models (BAMs) are an alternative to support this new trend. This paper proposes the BAMSDN (Band-width Allocation Model through Software-Defined Networking) framework that dynamically allocates resources (bandwidth) for a MPLS (MultiProtocol Label Switching) network using a SDN (Software-Defined Networking)/OpenFlow strategy with BAM. The framework adopts an innovative implementation approach for BAM systems by controlling the MPLS network using SDN with OpenFlow. Experimental results suggest that using SDN/OpenFlow with BAM for bandwidth allocation does have effective advantages for MPLS networks requiring flexible resource sharing among applications and facilitates the migration path to an SDN/OpenFlow network.