Local Update Algorithms for Random Graphs

Duchon, Philippe; Duvignau, Romaric

doi:10.1007/978-3-642-54423-1_32

Cited by 7 publications

(10 citation statements)

References 10 publications

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“…This links the current work to distributed algorithms able to match nodes using preferences [8,22]. The adaptation of such algorithms, and in particular greedy matchings [32], to the use-case presented here (i.e., with weights that require prediction and communication to estimate) while considering the stability of the network under arrival/departure of peers [5,9], would provide mechanisms to build those sharing communities in a distributed and efficient manner.…”

Section: Related Workmentioning

confidence: 99%

“…BDR can be understood as how many hours of average load can be stored by a given household. Production level: a pair (ALR,BDR) for a given household h. In the following, we will usually deduce the values of PV h and B h from a given production level, i.e., a production level of (3,5) for a household h with an average load of 1.5 kWh entails PV h = 3 × 1.5 = 4.5 kWp and B h = 5 × 1.5 = 7.5 kWh. Pure-consumer: a household with production level (0,0).…”

Section: Model 31 Definitionsmentioning

confidence: 99%

“…Individually, any household will have a tendency to prefer to be matched 5 with the available household that has the largest average load, once neglecting preferences as shown in Figure 6.…”

Section: What Influences Cooperative Gain In Amentioning

confidence: 99%

“…This advocates in this particular 20/100-community example to use larger peer neighborhoods, such as matching 3 to 4 consumers to a single prosumer to form 1/4-or 1/5-communities. 5 This is true if e.g., the gain offered by the pair would be shared between the pair and not with the rest of the pool, see Section 5.5. 6 averaged by enumerating all possible matchings for the 20-pool and after generating 10 7 random matchings in the 100-pool.…”

Section: What Influences Cooperative Gain In Amentioning

confidence: 99%

See 3 more Smart Citations

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

Duvignau

Heinisch

Göransson

et al. 2020

Proceedings of the Eleventh ACM International Conference on Future Energy Systems

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Due to ever lower cost, investments in renewable electricity generation and storage have become more attractive to electricity consumers in recent years. At the same time, electricity generation and storage have become something to share or trade locally in energy communities or microgrid systems. In this context, peerto-peer (P2P) sharing has gained attention, since it offers a way to optimize the cost-benefits from distributed resources, making them financially more attractive. However, it is not yet clear in which situations consumers do have interests to team up and how much cost is saved through cooperation in practical instances. While introducing realistic continuous decisions, through detailed analysis based on large-scale measured household data, we show that the financial benefit of cooperation does not require an accurate forecasting. Furthermore, we provide strong evidence, based on analysis of the same data, that even P2P networks with only 2-5 participants can reach a high fraction (96% in our study) of the potential gain, i.e., of the ideal offline (i.e., non-continuous) achievable gain. Maintaining such small communities results in much lower associated costs and better privacy, as each participant only needs to share its data with 1-4 other peers. These findings shed new light and motivate requirements for distributed, continuous and dynamic P2P matching algorithms for energy trading and sharing.

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Section: Related Workmentioning

confidence: 99%

Section: Model 31 Definitionsmentioning

confidence: 99%

Section: What Influences Cooperative Gain In Amentioning

confidence: 99%

Section: What Influences Cooperative Gain In Amentioning

confidence: 99%

See 2 more Smart Citations

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

Duvignau

Heinisch

Göransson

et al. 2020

Proceedings of the Eleventh ACM International Conference on Future Energy Systems

Self Cite

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“…Assuming that the cost of keeping additional edges available is linear to the sum of lengths of their availability intervals, we show a low cost construction. Other work for maintaining some structure or property like connectivity in probabilistic dynamic graphs includes [7,8].…”

Section: Our Contributionmentioning

confidence: 99%

On Verifying and Maintaining Connectivity of Interval Temporal Networks

Akrida

Spirakis

2019

Parallel Process. Lett.

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An interval temporal network is, informally speaking, a network whose links change with time. The term interval means that a link may exist for one or more time intervals, called availability intervals of the link, after which it does not exist (until, maybe, a further moment in time when it starts being available again). In this model, we consider continuous time and high-speed (instantaneous) information dissemination. An interval temporal network is connected during a period of time [Formula: see text], if it is connected for all time instances [Formula: see text] (instantaneous connectivity). In this work, we study instantaneous connectivity issues of interval temporal networks. We provide a polynomial-time algorithm that answers if a given interval temporal network is connected during a time period. If the network is not connected throughout the given time period, then we also give a polynomial-time algorithm that returns large components of the network that remain connected and remain large during [Formula: see text]; the algorithm also considers the components of the network that start as large at time [Formula: see text] but dis-connect into small components within the time interval [Formula: see text], and answers how long after time [Formula: see text] these components stay connected and large. Finally, we examine a case of interval temporal networks on tree graphs where the lifetimes of links and, thus, the failures in the connectivity of the network are not controlled by us; however, we can “feed” the network with extra edges that may re-connect it into a tree when a failure happens, so that its connectivity is maintained during a time period. We show that we can with high probability maintain the connectivity of the network for a long time period by making these extra edges available for re-connection using a randomized approach. Our approach also saves some cost in the design of availabilities of the edges; here, the cost is the sum, over all extra edges, of the length of their availability-to-reconnect interval.

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Expansion and flooding in dynamic random networks with node churn

Becchetti

Clementi

Pasquale

et al. 2023

Random Struct Algorithms

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We study expansion and flooding in evolving graphs, when nodes and edges are continuously created and removed. We consider a model with Poisson node inter-arrival and exponential node survival times. Upon joining the network, a node connects to 𝑑 = (1) random nodes, while an edge disappears whenever one of its endpoints leaves the network. For this model, we show that, although the graph has Ω 𝑑 (n) isolated nodes with large, constant probability, flooding still informs a fraction 1 − exp(−Ω(𝑑)) of the nodes in time (log n). Moreover, at any given time, the graph exhibits a "large-set expansion" property. We further consider a model in which each edge leaving the network is replaced by a fresh, random one. In this second case, we prove that flooding informs all nodes in time (log n), with high probability. Moreover, the graph is a vertex expander with high probability.

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Local Update Algorithms for Random Graphs

Cited by 7 publications

References 10 publications

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

On Verifying and Maintaining Connectivity of Interval Temporal Networks

Expansion and flooding in dynamic random networks with node churn

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