HAGP: A Hub-Centric Asynchronous Graph Processing Framework for Scale-Free Graph

2015 ◽  
Author(s):  
Tao Gao ◽  
Yutong Lu ◽  
Baida Zhang
Keyword(s):  
Graph Processing ◽  
Free Graph ◽  
Scale Free ◽  
Processing Framework

VGL: a high-performance graph processing framework for the NEC SX-Aurora TSUBASA vector architecture

2021 ◽  
Author(s):  
Ilya V. Afanasyev ◽  
Vladimir V. Voevodin ◽  
Kazuhiko Komatsu ◽  
Hiroaki Kobayashi
Keyword(s):  
High Performance ◽  
Graph Processing ◽  
Processing Framework

scholarly journals BLADYG: A Graph Processing Framework for Large Dynamic Graphs

2017 ◽  
Vol 9 ◽  
pp. 9-17 ◽  
Author(s):  
Sabeur Aridhi ◽  
Alberto Montresor ◽  
Yannis Velegrakis
Keyword(s):  
Graph Processing ◽  
Dynamic Graphs ◽  
Processing Framework

scholarly journals Thek-Core and Branching Processes

2008 ◽  
Vol 17 (1) ◽  
pp. 111-136 ◽  
Author(s):  
OLIVER RIORDAN
Keyword(s):  
Power Law ◽  
Branching Process ◽  
Minimum Degree ◽  
Branching Processes ◽  
Threshold Value ◽  
Free Graph ◽  
Scale Free ◽  
Asymptotic Size ◽  
Inhomogeneous Random Graphs ◽  
Local Coupling

Thek-coreof a graphGis the maximal subgraph ofGhaving minimum degree at leastk. In 1996, Pittel, Spencer and Wormald found the threshold λcfor the emergence of a non-trivialk-core in the random graphG(n, λ/n), and the asymptotic size of thek-core above the threshold. We give a new proof of this result using a local coupling of the graph to a suitable branching process. This proof extends to a general model of inhomogeneous random graphs with independence between the edges. As an example, we study thek-core in a certain power-law or ‘scale-free’ graph with a parameterccontrolling the overall density of edges. For eachk≥ 3, we find the threshold value ofcat which thek-core emerges, and the fraction of vertices in thek-core whencis ϵ above the threshold. In contrast toG(n, λ/n), this fraction tends to 0 as ϵ→0.

scholarly journals Erdos – Renyi Random Graph and Machine Learning Based Botnet Detection

2020 ◽  
Vol 9 (5) ◽  
pp. 1037-1040
Keyword(s):  
Machine Learning ◽  
Probability Theory ◽  
Graph Theory ◽  
Complex Network ◽  
Random Graphs ◽  
Connected Graph ◽  
Free Graph ◽  
Botnet Detection ◽  
Scale Free ◽  
Made In

Basically large networks are prone to attacks by bots and lead to complexity. When the complexity occurs then it is difficult to overcome the vulnerability in the network connections. In such a case, the complex network could be dealt with the help of probability theory and graph theory concepts like Erdos – Renyi random graphs, Scale free graph, highly connected graph sequences and so on. In this paper, Botnet detection using Erdos – Renyi random graphs whose patterns are recognized as the number of connections that the vertices and edges made in the network is proposed. This paper also presents the botnet detection concepts based on machine learning.

scholarly journals Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

2022 ◽  
Vol 27 (1) ◽  
pp. 1-30
Author(s):  
Mengke Ge ◽  
Xiaobing Ni ◽  
Xu Qi ◽  
Song Chen ◽  
Jinglei Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Small World ◽  
Brain Network ◽  
Graph Processing ◽  
Hop Count ◽  
Scale Free ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

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