Power Aware Heterogeneous Node Assembly

Author(s):  
Bilge Acun ◽  
Alper Buyuktosunoglu ◽  
Eun Kyung Lee ◽  
Yoonho Park
Keyword(s):  
Author(s):  
S. Moore ◽  
T. Rogers

Having knowledge of the contact network over which an infection is spreading opens the possibility of making individualized predictions for the likelihood of different nodes to become infected. When multiple infective strains attempt to spread simultaneously we may further ask which strain, or strains, are most likely to infect a particular node. In this article we investigate the heterogeneity in likely outcomes for different nodes in two models of multi-type epidemic spreading processes. For models allowing co-infection we derive message-passing equations whose solution captures how the likelihood of a given node receiving a particular infection depends on both the position of the node in the network and the interaction between the infection types. For models of competing epidemics in which co-infection is impossible, a more complicated analysis leads to the simpler result that node vulnerability factorizes into a contribution from the network topology and a contribution from the infection parameters.


2020 ◽  
Vol 101 (2) ◽  
Author(s):  
O. Smith ◽  
J. Crowe ◽  
E. Farcot ◽  
R. D. O'Dea ◽  
K. I. Hopcraft

2011 ◽  
Vol E94-B (8) ◽  
pp. 2254-2264 ◽  
Author(s):  
Sampath PRIYANKARA ◽  
Kazuhiko KINOSHITA ◽  
Hideki TODE ◽  
Koso MURAKAMI

2016 ◽  
Vol 21 (2) ◽  
pp. 43-54
Author(s):  
Ryosuke Morita ◽  
Chisa Takano ◽  
Masaki Aida

Abstract In large scale mobile ad hoc networks (MANETs), it is effective to reduce the load of routing by introducing hierarchical routing, and it is conducted by clustering of nodes. A clustering mechanism based on the diffusion equation is a typical autonomous clustering in MANETs, and gives appropriate clustering if all the node degrees are uniform. However, node degrees in MANETs are heterogeneous in general, the fact causes the difference in the strength of diffusion effect. This difference causes that the position of cluster head tends to be around the boundary of networks and degrade battery efficiency of nodes. In this paper, by introducing an asymmetric diffusion depending on node degree, we propose a new clustering method independent of heterogeneity of node degrees. We show that the proposed method has efficient characteristics for battery consumption. In addition, we show the comparison of the proposed method with the conventional method with respect to the efficiency of routing.


2021 ◽  
Vol 7 (28) ◽  
pp. eabh1303
Author(s):  
Philip S. Chodrow ◽  
Nate Veldt ◽  
Austin R. Benson

Hypergraphs are a natural modeling paradigm for networked systems with multiway interactions. A standard task in network analysis is the identification of closely related or densely interconnected nodes. We propose a probabilistic generative model of clustered hypergraphs with heterogeneous node degrees and edge sizes. Approximate maximum likelihood inference in this model leads to a clustering objective that generalizes the popular modularity objective for graphs. From this, we derive an inference algorithm that generalizes the Louvain graph community detection method, and a faster, specialized variant in which edges are expected to lie fully within clusters. Using synthetic and empirical data, we demonstrate that the specialized method is highly scalable and can detect clusters where graph-based methods fail. We also use our model to find interpretable higher-order structure in school contact networks, U.S. congressional bill cosponsorship and committees, product categories in copurchasing behavior, and hotel locations from web browsing sessions.


2009 ◽  
Vol 8 (8) ◽  
pp. 1132-1147 ◽  
Author(s):  
T. Spyropoulos ◽  
T. Turletti ◽  
K. Obraczka

Sign in / Sign up

Export Citation Format

Share Document