Routing in Large-scale Dynamic Networks

2020 ◽  
Vol 20 (4) ◽  
pp. 1-24
Author(s):  
Weichao Gao ◽  
James Nguyen ◽  
Yalong Wu ◽  
William G. Hatcher ◽  
Wei Yu
Keyword(s):  
Large Scale ◽  
Dynamic Networks

Context-aware Distance Measures for Dynamic Networks

2022 ◽  
Vol 16 (1) ◽  
pp. 1-34
Author(s):  
Yiji Zhao ◽  
Youfang Lin ◽  
Zhihao Wu ◽  
Yang Wang ◽  
Haomin Wen
Keyword(s):  
Large Scale ◽  
Dynamic Networks ◽  
Distance Measures ◽  
General Definition ◽  
Mathematical Representation ◽  
Network Clustering ◽  
Context Aware ◽  
Network Distance ◽  
Practical Applications ◽  
Spectral Distance

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

Peer-to-Peer Service Discovery for Grid Computing

2012 ◽  
pp. 232-259
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Franck Petit ◽  
Cédric Tedeschi
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Service Discovery ◽  
Large Scale ◽  
Dynamic Networks ◽  
Peer To Peer ◽  
Indexing System ◽  
Key Points ◽  
Computing Platforms ◽  
Traditional Approaches

Within distributed computing platforms, some computing abilities (or services) are offered to clients. To build dynamic applications using such services as basic blocks, a critical prerequisite is to discover those services. Traditional approaches to the service discovery problem have historically relied upon centralized solutions, unable to scale well in large unreliable platforms. In this chapter, we will first give an overview of the state of the art of service discovery solutions based on peer-to-peer (P2P) technologies that allow such a functionality to remain efficient at large scale. We then focus on one of these approaches: the Distributed Lexicographic Placement Table (DLPT) architecture, that provide particular mechanisms for load balancing and fault-tolerance. This solution centers around three key points. First, it calls upon an indexing system structured as a prefix tree, allowing multi-attribute range queries. Second, it allows the mapping of such structures onto heterogeneous and dynamic networks and proposes some load balancing heuristics for it. Third, as our target platform is dynamic and unreliable, we describe its powerful fault-tolerance mechanisms, based on self-stabilization. Finally, we present the software prototype of this architecture and its early experiments.

scholarly journals A dynamical systems view of network centrality

2014 ◽  
Vol 470 (2165) ◽  
pp. 20130835 ◽  
Author(s):  
Peter Grindrod ◽  
Desmond J. Higham
Keyword(s):  
Dynamical Systems ◽  
Large Scale ◽  
Dynamic Networks ◽  
Network Evolution ◽  
Mathematical Framework ◽  
The Novel ◽  
Matrix Logarithm ◽  
The Matrix ◽  
Systems View ◽  
Logarithm Function

To gain insights about dynamic networks, the dominant paradigm is to study discrete snapshots , or timeslices , as the interactions evolve. Here, we develop and test a new mathematical framework where network evolution is handled over continuous time, giving an elegant dynamical systems representation for the important concept of node centrality. The resulting system allows us to track the relative influence of each individual. This new setting is natural in many digital applications, offering both conceptual and computational advantages. The novel differential equations approach is convenient for modelling and analysis of network evolution and gives rise to an interesting application of the matrix logarithm function. From a computational perspective, it avoids the awkward up-front compromises between accuracy, efficiency and redundancy required in the prevalent discrete-time setting. Instead, we can rely on state-of-the-art ODE software, where discretization takes place adaptively in response to the prevailing system dynamics. The new centrality system generalizes the widely used Katz measure, and allows us to identify and track, at any resolution, the most influential nodes in terms of broadcasting and receiving information through time-dependent links. In addition to the classical static network notion of attenuation across edges, the new ODE also allows for attenuation over time, as information becomes stale. This allows ‘running measures’ to be computed, so that networks can be monitored in real time over arbitrarily long intervals. With regard to computational efficiency, we explain why it is cheaper to track good receivers of information than good broadcasters. An important consequence is that the overall broadcast activity in the network can also be monitored efficiently. We use two synthetic examples to validate the relevance of the new measures. We then illustrate the ideas on a large-scale voice call network, where key features are discovered that are not evident from snapshots or aggregates.

scholarly journals Nonparametric link prediction in large scale dynamic networks

2014 ◽  
Vol 8 (2) ◽  
pp. 2022-2065 ◽  
Author(s):  
Purnamrita Sarkar ◽  
Deepayan Chakrabarti ◽  
Michael Jordan
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Dynamic Networks

Weak State Routing for Large-Scale Dynamic Networks

2010 ◽  
Vol 18 (5) ◽  
pp. 1450-1463 ◽  
Author(s):  
Utku Günay Acer ◽  
Shivkumar Kalyanaraman ◽  
Alhussein A. Abouzeid
Keyword(s):  
Large Scale ◽  
Dynamic Networks ◽  
Weak State

scholarly journals Large Scale Evolving Graphs with Burst Detection

2019 ◽  
Author(s):  
Yifeng Zhao ◽  
Xiangwei Wang ◽  
Hongxia Yang ◽  
Le Song ◽  
Jie Tang
Keyword(s):  
Social Networks ◽  
Recurrent Events ◽  
Large Scale ◽  
Dynamic Networks ◽  
Temporal Patterns ◽  
Simulated Dataset ◽  
Burst Detection ◽  
Evolving Graphs

Analyzing large-scale evolving graphs are crucial for understanding the dynamic and evolutionary nature of social networks. Most existing works focus on discovering repeated and consistent temporal patterns, however, such patterns cannot fully explain the complexity observed in dynamic networks. For example, in recommendation scenarios, users sometimes purchase products on a whim during a window shopping.Thus, in this paper, we design and implement a novel framework called BurstGraph which can capture both recurrent and consistent patterns, and especially unexpected bursty network changes. The performance of the proposed algorithm is demonstrated on both a simulated dataset and a world-leading E-Commerce company dataset, showing that they are able to discriminate recurrent events from extremely bursty events in terms of action propensity.

scholarly journals Dynamic Networks in the Emotional Brain

2016 ◽  
Vol 23 (4) ◽  
pp. 383-396 ◽  
Author(s):  
Luiz Pessoa ◽  
Brenton McMenamin
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Dynamic Networks ◽  
Brain Regions ◽  
Time Varying ◽  
Brain Circuits ◽  
Mental Faculty ◽  
Dynamic View ◽  
The Impact ◽  
Perception Action

Research on the emotional brain has often focused on a few structures thought to be central to this type of processing—hypothalamus, amygdala, insula, and so on. Conceptual thinking about emotion has viewed this mental faculty as linked to broader brain circuits, too, including early ideas by Papez and others. In this article, we discuss research that embraces a distributed view of emotion circuits and efforts to unravel the impact on emotional manipulations on the processing of several large-scale brain networks that are chiefly important for mental operations traditionally labeled with terms such as “perception,” “action,” and “cognition.” Furthermore, we describe networks as dynamic processes and how emotion-laden stimuli strongly affect network structure. As networks are not static entities, their organization unfolds temporally, such that specific brain regions affiliate with them in a time-varying fashion. Thus, at a specific moment, brain regions participate more strongly in some networks than others. In this dynamic view of brain function, emotion has broad, distributed effects on processing in a manner that transcends traditional boundaries and inflexible labels, such as “emotion” and “cognition.” What matters is the coordinated action that supports behaviors.

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