Dynamic networks

Kühn, Fabian; Oshman, Rotem

doi:10.1145/1959045.1959064

Cited by 164 publications

(14 citation statements)

References 43 publications

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“…Moreover, Haeupler and Karger, in [8], studied how to use network coding to expedite the information dissemination in this model. We direct readers to [15] for the state-of-the-art up to the end of 2010.…”

Section: Related Workmentioning

confidence: 99%

“…The adversary is very strong in the sense that nodes do not know who their neighbors are for the current round before they broadcast their messages. This model is appealing in the sense that it captures widely-used mobile and wireless networks where communication can be unpredictable (see [19,15] for details). Our main objective in this present work is to understand the complexity of the distributed queuing problem in this adversarial dynamic network model.…”

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confidence: 99%

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Distributed Queuing in Dynamic Networks

Sharma

Busch

2013

Electron. Proc. Theor. Comput. Sci.

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We consider the problem of forming a distributed queue in the adversarial dynamic network model of Kuhn, Lynch, and Oshman (STOC 2010) in which the network topology changes from round to round but the network stays connected. This is a synchronous model in which network nodes are assumed to be fixed, the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors are for the current round before they broadcast their messages. Queue requests may arrive over rounds at arbitrary nodes and the goal is to eventually enqueue them in a distributed queue. We present two algorithms that give a total distributed ordering of queue requests in this model. We measure the performance of our algorithms through round complexity, which is the total number of rounds needed to solve the distributed queuing problem. We show that in 1-interval connected graphs, where the communication links change arbitrarily between every round, it is possible to solve the distributed queueing problem in O(nk) rounds using O(log n) size messages, where n is the number of nodes in the network and k <= n is the number of queue requests. Further, we show that for more stable graphs, e.g. T-interval connected graphs where the communication links change in every T rounds, the distributed queuing problem can be solved in O(n+ (nk/min(alpha,T))) rounds using the same O(log n) size messages, where alpha > 0 is the concurrency level parameter that captures the minimum number of active queue requests in the system in any round. These results hold in any arbitrary (sequential, one-shot concurrent, or dynamic) arrival of k queue requests in the system. Moreover, our algorithms ensure correctness in the sense that each queue request is eventually enqueued in the distributed queue after it is issued and each queue request is enqueued exactly once. We also provide an impossibility result for this distributed queuing problem in this model. To the best of our knowledge, these are the first solutions to the distributed queuing problem in adversarial dynamic networks

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Distributed Queuing in Dynamic Networks

Sharma

Busch

2013

Electron. Proc. Theor. Comput. Sci.

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“…To cope with frequent topology changes of dynamic networks, quite a lot of work has been studied, including new system models and distributed algorithms. Distributed algorithms are designed to directly solve fundamental distributed computing problems such as naming, counting, and information dissemination [5][6][7][8][9] in dynamic networks. System models define and constrain the behaviors of underlying nodes or processes in a distributed system via abstractive assumptions and properties.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

2018

Information

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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.

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“…Dynamic Network Analysis is a subfield of Network Science aiming at representing and studying the behavior of systems constituted of interacting or related objects evolving through time. This domain gained attention lately, as attested by the recent publication of several surveys (Aggarwal and Subbian 2014;Blonder et al 2012;Holme and Saramäki 2012;Kuhn and Oshman 2011;Spiliopoulou 2011).…”

Section: Introductionmentioning

confidence: 99%

Exploring the evolution of node neighborhoods in Dynamic Networks

Orman

Labatut

Naskalı

2017

Physica A: Statistical Mechanics and its Applications

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Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes. density, network diameter (Leskovec et al. 2005a). Second, mesoscopic methods consider the network at an intermediary level, generally that of the community structure, e.g. modularity measure (Kashtan and Alon 2005), size of the communities (Backstrom et al. 2006). Finally, microscopic methods focus on individual nodes and possibly their direct neighborhood, e.g. clustering coefficient (Clauset and Eagle 2007).By definition, methods specifically designed to handle a dynamic network are more likely to take advantage of the specificity of such data. Regarding the granularity, if macroscopic and mesoscopic methods are widespread in the literature, it is not the case for microscopic methods. Yet, the benefits of such approaches are numerous: by allowing the tracking of finer evolution processes, they complement macroscopic and/or mesoscopic results. They help identifying behavioral trends among the network nodes, and consequently outliers. These results can facilitate the description and understanding of processes observed at a higher level, and can be useful to define models of the studied system, especially agent-based ones.In this work, we propose a method specifically designed to study dynamic slowly evolving networks, at the microscopic level. We characterize a node by the evolution of its neighborhood. More precisely, we detect specific events occurring among the groups of nodes constituting this neighborhood, between each pair of consecutive time slices, which we call neighborhood evolution events. This method constitutes our main contribution. To the best of our knowledge, it is the fir...

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Dynamic networks

Cited by 164 publications

References 43 publications

Distributed Queuing in Dynamic Networks

Distributed Queuing in Dynamic Networks

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

Exploring the evolution of node neighborhoods in Dynamic Networks

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