scholarly journals Distributed computation in dynamic networks via random walks

2015 ◽  
Vol 581 ◽  
pp. 45-66 ◽  
Author(s):  
Atish Das Sarma ◽  
Anisur Rahaman Molla ◽  
Gopal Pandurangan
Author(s):  
Mark Newman

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.


2009 ◽  
Vol 3 (4) ◽  
pp. 266-278 ◽  
Author(s):  
G.S. Thakur ◽  
A.W.M. Dress ◽  
R. Tiwari ◽  
S.-S. Chen ◽  
M.T. Thai

2016 ◽  
Vol 103 ◽  
pp. 165-180 ◽  
Author(s):  
Víctor M. López Millán ◽  
Vicent Cholvi ◽  
Antonio Fernández Anta ◽  
Luis López

2016 ◽  
pp. cnw009
Author(s):  
Giulio Iacobelli ◽  
Daniel Ratton Figueiredo

2012 ◽  
Vol 23 (04) ◽  
pp. 803-830 ◽  
Author(s):  
ALAIN BUI ◽  
ABDURUSUL KUDIRETI ◽  
DEVAN SOHIER

In this paper, we present a fully distributed random walk based clustering algorithm intended to work on dynamic networks of arbitrary topologies. A bounded-size core is built through a random walks based procedure. Its neighboring nodes that do not belong to any cluster are recruited by the core as ordinary nodes. Using cores allow us to formulate constraints on the clustering and fulfill them on any topology. The correctness and termination of our algorithm are proven. We also prove that when two clusters are adjacent, at least one of them has a complete core (i.e. a core with the maximum size allowed by the parameter). One of the important advantages of our mobility-adaptive algorithm is that the re-clustering is local: the management of the connections or disconnections of links and reorganization of nodes affect only the clusters in which they are, possibly adjacent clusters, and at worst, the ordinary nodes of the clusters adjacent to the neighboring clusters. This allows us to bound the diameter of the portion of the network that is affected by a topological change.


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