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Explore the concepts of condensation and coevolving dynamics in network theory, covering topics like random walks, zero-range processes, and synaptic plasticity in evolving networks. Understand the dynamics of particles moving and interacting within networks, leading to phenomena like dynamic instabilities and phase transitions. The analysis includes numerical data, analytical theories, and a flow diagram to summarize key findings.
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Condensation in/of Networks Jae Dong Noh NSPCS08, 1-4 July, 2008, KIAS
Getting wired • Moving and Interacting • Being rewired
References • Random walks • Noh and Rieger, PRL92, 118701 (2004). • Noh and Kim, JKPS48, S202 (2006). • Zero-range processes • Noh, Shim, and Lee, PRL94, 198701 (2005). • Noh, PRE72, 056123 (2005). • Noh, JKPS50, 327 (2007). • Coevolving networks • Kim and Noh, PRL100, 118702 (2008). • Kim and Noh, in preparation (2008).
Basic Concepts • Network = {nodes} [ {links} • Adjacency matrix A • Degree of a node i : • Degree distribution • Scale-free networks :
1/5 1/5 1/5 1/5 1/5 Definition • Random motions of a particle along links • Random spreading
Stationary State Property • Detailed balance : • Stationary state probability distribution
SF networks w/o loops SF networks with many loops Relaxation Dynamics • Return probability
Mean First Passage Time • MFPT
Model • Interacting particle system on networks • Each site may be occupied by multiple particles • Dynamics : At each node i , • A single particle jumps out of i at the rate ui (ni ), • and hops to a neighboring node j selected randomly with the probability Wji .
transport capacity particle interactions Jumping rate ui (n) Hopping probability Wji • depends only on the occupation number at the departing site. • may be different for different sites (quenched disorder) independent of the occupation numbers at the departing and arriving sites Model Note that [ZRP with M=1 particle] = [ single random walker] [ZRP with u(n) = n ] = [ M indep. random walkers]
Stationary State Property [M.R. Evans, Braz. J. Phys. 30, 42 (2000)] • Stationary state probability distribution : product state • PDF at node i : where e.g.,
Condensation in ZRP • Condensation : single (multiple) node(s) is (are) occupied by a macroscopic number of particles • Condition for the condensation in lattices • Quenched disorder (e.g., uimp. = <1, ui≠imp. = 1) • On-site attractive interaction : if the jumping rate function ui(n) = u(n) decays ‘faster’ than ~(1+2/n) e.g.,
ZRP on SF Networks • Scale-free networks • Jumping rate • (δ>1) : repulsion • (δ=1) : non-interacting • (δ<1) : attraction • Hopping probability : random walks
Condensation on SF Networks • Stationary state probability distribution • Mean occupation number
normal phase transition line condensed phase Phase Diagram Complete condensation
Synaptic Plasticity • In neural networks • Bio-chemical signal transmission from neural to neural through synapses • Synaptic coupling strength may be enhanced (LTP) or suppressed (LTD) depending on synaptic activities • Network evolution
2 3 1 2 4 5 3 4 Co-evolving Network Model • Weighted undirected network + diffusing particles • Particles dynamics : random diffusion • Weight dynamics [LTP] • Link dynamics [LTD]: With probability 1/we, each link e is removed and replaced by a new one
Dynamic Instability • Due to statistical fluctuations, a node ‘hub’ may have a higher degree than others • Particles tend to visit the ‘hub’ more frequently • Links attached to the ‘hub’ become more robust, hence the hub collects more links than other nodes • Positive feedback dynamic instability toward the formation of hubs
dynamic instability [N=1000, <k>=4] linear growth sub-linear growth Numerical Data for kmax dynamic phase transition
Poissonian + Poissonian + Isolated hubs Poissonian + Fat-tailed Degree Distribution high density low density
Analytic Theory • Separation of time scales • particle dynamics : short time scale • network dynamics : long time scale • Integrating out the degrees of freedom of particles • Effective network dynamics : Non-Markovian queueing (balls-in-boxes) process
1 K i 2 queue Non-Markovian Queueing Process • node i $ queue (box) • edge $ packet (ball) • degree k $ queue size K
queue Non-Markovian Queueing Process • Weight of a ball • A ball leaves a queue with the probability
Outgoing Particle Flux ~ uZRP(K) • Upper bound for fout(K,)
queue is trapped at K=K1 for • instability time t = • - queue grows linearly after t > Dynamic Phase Transition
Phase Diagram ballistic growth of hub sub-linear growth of hub
2 3 1 2 4 5 3 4 A Variant Model • Weighted undirected network + diffusing particles • Particles dynamics : random diffusion • Weight dynamics • Link dynamics : Rewiring with probability 1/we • Weight regularization :
Rate equations for K and w 1 K i potential candidate for the hub 2 A Simplified Theory
no hub hub no condensation condensation Flow Diagram
Summary • Dynamical systems on networks • random walks • zero range process • Coevolving network models • Network heterogeneity $ Condensation