Abstract
| Original language | English |
|---|---|
| Article number | 158301 |
| Number of pages | 5 |
| Journal | Physical review letters |
| Volume | 120 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - 9 Apr 2018 |
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Keywords
- random walk
- complex network
- crowding
- network reconstruction
- non linear diffusion
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Hopping in the Crowd to Unveil Network Topology. / Asllani, Malbor; Carletti, Timoteo; Di Patti, Francesca ; Fanelli, Duccio; Piazza, Francesco.
In: Physical review letters, Vol. 120, No. 15, 158301, 09.04.2018.Research output: Contribution to journal › Letter
TY - JOUR
T1 - Hopping in the Crowd to Unveil Network Topology
AU - Asllani, Malbor
AU - Carletti, Timoteo
AU - Di Patti, Francesca
AU - Fanelli, Duccio
AU - Piazza, Francesco
PY - 2018/4/9
Y1 - 2018/4/9
N2 - We introduce a nonlinear operator to model diffusion on a complex undirected network under crowded conditions. We show that the asymptotic distribution of diffusing agents is a nonlinear function of the nodes’ degree and saturates to a constant value for sufficiently large connectivities, at variance with standard diffusion in the absence of excluded-volume effects. Building on this observation, we define and solve an inverse problem, aimed at reconstructing the a priori unknown connectivity distribution. The method gathers all the necessary information by repeating a limited number of independent measurements of the asymptotic density at a single node, which can be chosen randomly. The technique is successfully tested against both synthetic and real data and is also shown to estimate with great accuracy the total number of nodes.
AB - We introduce a nonlinear operator to model diffusion on a complex undirected network under crowded conditions. We show that the asymptotic distribution of diffusing agents is a nonlinear function of the nodes’ degree and saturates to a constant value for sufficiently large connectivities, at variance with standard diffusion in the absence of excluded-volume effects. Building on this observation, we define and solve an inverse problem, aimed at reconstructing the a priori unknown connectivity distribution. The method gathers all the necessary information by repeating a limited number of independent measurements of the asymptotic density at a single node, which can be chosen randomly. The technique is successfully tested against both synthetic and real data and is also shown to estimate with great accuracy the total number of nodes.
KW - random walk
KW - complex network
KW - crowding
KW - network reconstruction
KW - non linear diffusion
UR - http://www.scopus.com/inward/record.url?scp=85045303944&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.120.158301
DO - 10.1103/PhysRevLett.120.158301
M3 - Letter
VL - 120
JO - Physical review letters
JF - Physical review letters
SN - 0031-9007
IS - 15
M1 - 158301
ER -