scholarly journals Adaptive Partitioning for Large-Scale Dynamic Graphs

2014 ◽  
Author(s):  
Luis M. Vaquero ◽  
Felix Cuadrado ◽  
Dionysios Logothetis ◽  
Claudio Martella
Keyword(s):  
Large Scale ◽  
Dynamic Graphs ◽  
Adaptive Partitioning

scholarly journals Top-$k$ Subgraph Query Based on Frequent Structure in Large-Scale Dynamic Graphs

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 78471-78482
Author(s):  
Xiaohuan Shan ◽  
Guangxiang Wang ◽  
Linlin Ding ◽  
Baoyan Song ◽  
Yan Xu
Keyword(s):  
Large Scale ◽  
Dynamic Graphs ◽  
Subgraph Query

scholarly journals Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248764
Author(s):  
Angelo Furno ◽  
Nour-Eddin El Faouzi ◽  
Rajesh Sharma ◽  
Eugenio Zimeo
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Large Scale ◽  
Transportation Networks ◽  
Smart Cities ◽  
Dynamic Networks ◽  
Computation Time ◽  
Weighted Graphs ◽  
Dynamic Graphs ◽  
Physical Infrastructure

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to identify the components (nodes) of a system modelled as a graph that are mostly traversed by information flows thus being critical to the proper functioning of the system itself. In the transportation domain, the metric has been mainly adopted to discover topological bottlenecks of the physical infrastructure composed of roads or railways. The adoption of this metric to study the evolution of transportation networks that take into account also the dynamic conditions of traffic is in its infancy mainly due to the high computation time needed to compute BC in large dynamic graphs. This paper explores the adoption of dynamic BC, i.e., BC computed on dynamic large-scale graphs, modeling road networks and the related vehicular traffic, and proposes the adoption of a fast algorithm for ahead monitoring of transportation networks by computing approximated BC values under time constraints. The experimental analysis proves that, with a bounded and tolerable approximation, the algorithm computes BC on very large dynamically weighted graphs in a significantly shorter time if compared with exact computation. Moreover, since the proposed algorithm can be tuned for an ideal trade-off between performance and accuracy, our solution paves the way to quasi real-time monitoring of highly dynamic networks providing anticipated information about possible congested or vulnerable areas. Such knowledge can be exploited by travel assistance services or intelligent traffic control systems to perform informed re-routing and therefore enhance network resilience in smart cities.

scholarly journals Communication-Optimal Distributed Dynamic Graph Clustering

2019 ◽  
Vol 33 ◽  
pp. 5957-5964
Author(s):  
Chun Jiang Zhu ◽  
Tan Zhu ◽  
Kam-Yiu Lam ◽  
Song Han ◽  
Jinbo Bi
Keyword(s):  
Message Passing ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Real Life ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Communication Models ◽  
Clustering Quality ◽  
Remote Sites ◽  
Time Point

We consider the problem of clustering graph nodes over large-scale dynamic graphs, such as citation networks, images and web networks, when graph updates such as node/edge insertions/deletions are observed distributively. We propose communication-efficient algorithms for two well-established communication models namely the message passing and the blackboard models. Given a graph with n nodes that is observed at s remote sites over time [1,t], the two proposed algorithms have communication costs Õ(ns) and Õ(n + s) (Õ hides a polylogarithmic factor), almost matching their lower bounds, Ω(ns) and Ω(n + s), respectively, in the message passing and the blackboard models. More importantly, we prove that at each time point in [1,t] our algorithms generate clustering quality nearly as good as that of centralizing all updates up to that time and then applying a standard centralized clustering algorithm. We conducted extensive experiments on both synthetic and real-life datasets which confirmed the communication efficiency of our approach over baseline algorithms while achieving comparable clustering results.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

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