September 19  22, 2016
CCS 2016
Complex Systems Society
Beurs van Berlage,
Amsterdam
SUMMARY
The Conference on Complex Systems (CCS) has become a major venue for the
Complex Systems community since 2003.
After last year success in USA, we are now back in Europe.
AMSTERDAM CCS 2016, will be the major international conference and event for
complex systems and interdisciplinary science.
ORGANISING COMMITTEE CCS
Peter Sloot (chair) 
University of Amsterdam / Nanyang Technological University, Singapore 


Stephan Lansin 
Nanyang Technilogical University, Singapore 
Stephan Thurner 
Medical University of Vienna 
Saskia Werners 
Wageningen University 
Rick Quax 
University of Amsterdam 
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The conference will consist of keynote lectures,
parallel sessions and 49 satellite sessions.
Satellite session 49 will be on
"Fundamentals of
Networks"
Organised by Remco van der Hofstad (TU/e), Frank den
Hollander (UL), Diego Garlaschelli (UL)
PROGRAMME
WEDNESDAY SEPTEMBER 21
Diego Garlaschelli
Breaking of Ensemble Equivalence in Networks
It is generally believed that, in the thermodynamic limit, the
microcanonical description as a function of energy coincides with the
canonical description as a function of temperature. However, various
examples of systems for which the microcanonical and canonical ensembles are
not equivalent have been identified. A complete theory of this intriguing
phenomenon is still missing. Here we show that ensemble nonequivalence can
manifest itself also in random graphs with topological constraints. We find
that, while graphs with a given number of links are ensemble equivalent,
graphs with a given degree sequence are not. This result holds irrespective
of whether the energy is nonadditive (as in unipartite graphs) or additive
(as in bipartite graphs). In contrast with previous expectations, our
results show that (1) physically, nonequivalence can be induced by an
extensive number of local constraints, and not necessarily by longrange
interactions or nonadditivity, (2) mathematically, nonequivalence is
determined by a different largedeviation behavior of microcanonical and
canonical probabilities for a single microstate, and not necessarily for
almost all microstates. The latter criterion, which is entirely local, is
not restricted to networks and holds in general.
(joint work with Tiziano Squartini, Joey de Mol and Frank den Hollander)
Frank den Hollander
Mixing times of random walks on dynamic configuration models
The mixing time of a Markov chain is the time it needs to approach its
stationary distribution. For random walks on graphs, the characterisation of
the mixing time has been the subject of intensive study. One of the
motivations is the fact that the mixing time gives information about the
geometry of the graph. In the last few years, much attention has been
devoted to the analysis of mixing times for random walks on random graphs,
which poses interesting challenges.
Many realworld networks are dynamic in nature. It is therefore natural to
study random walks on dynamic random graphs. In this talk we investigate
what happens for simple random walk on a dynamic version of the
configuration model in which, at each unit of time, a fraction $\alpha_n$ of
the edges is randomly relocated, where $n$ is the number of nodes. For
degree distributions that converge and have a second moment that is bounded
in $n$, we show that the mixing time is of order $1/\sqrt{\alpha_n}$,
provided $\lim_{n\to\infty} \alpha_n(\log n)^2=\infty$. We identify the
sharp asymptotics of the mixing time when we additionally require that $\lim_{n\to\infty}
\alpha_n=0$, and relate the relevant proportionality constant to the average
probability of escape from the root by a simple random walk on an augmented
GaltonWatson tree that is obtained by taking a GaltonWatson tree whose
offspring distribution is the sizebiased version of the limiting degree
distribution and attaching to its root another GaltonWatson tree with the
same offspring distribution. Proofs are based on a randomised stopping time
argument in combination with coupling estimates.
(joint work with Luca Avena (Leiden), Hakan Guldas (Leiden) and Remco van
der Hofstad (Eindhoven))
Dmitri Krioukov
Networks with strong homogeneous clustering are geometric
Two common features of many large real networks are that they are sparse and
that they have strong clustering, i.e., large number of triangles
homogeneously distributed across all nodes. In many growing networks for
which historical data is available, the average degree and clustering are
roughly independent of the growing network size. Recently, latentspace
random graph models, also known as (soft) random geometric graphs, have been
used successfully to model these features of real networks, to predict
missing and future links in them, and to study their navigability, with
applications ranging from designing optimal routing in the Internet, to
identification of the informationtransmission skeleton in the human brain.
Yet it remains unclear if latentspace models are indeed adequate models of
real networks, as these models may have properties that real networks do not
have, or vice versa.
We show that maximumentropy random graphs in which the expected numbers of
edges and triangles at every node are fixed to constants, are approximately
soft random geometric graphs on the real line. The approximation is exact in
the limit of standard random geometric graphs with a sharp connectivity
threshold and strongest clustering. This result implies that a large number
of triangles homogeneously distributed across all vertices is not only
necessary but also a sufficient condition for the presence of a
latent/effective metric space in large sparse networks. Strong clustering,
ubiquitously observed in real networks, is thus a reflection of their latent
geometry.
Michel Mandjes
Scaling Limits for Stochastic Networks
In this talk I will sketch a body of recent results
obtained in the context of stochastic networks of dependently operating
resources. These could be thought of to represent reallife networks of all
sorts, such as traffic or communication networks, but Išll point out that
this setup is also highly relevant in economic and biological applications.
The underlying model can be thought of as a network of interacting
resources, which can be modeled in a discrete statespace context through
coupled queues, and in a continuous statespace context through specific
systems of stochastic differential equations; the individual resources
operate dependently as they react to the same environmental process.
For such large networks, one would typically like to
describe their dynamic behavior, and to devise procedures that can deal with
various undesired events (link failures, sudden overload, etc.). Išll show
how for systems that do not allow explicit analyses, various parameter
scalings help shedding light on their behavior. More specifically, I'll
discuss situations in which the timescale corresponding to the fluctuations
of the available resources differs from that of the fluctuations of the
customer's demand, leading to various appealing limit results.
Lex Schrijver
The partial disjoint paths problem
The partially disjoint paths problem asks for
connections between given pairs of terminals in a network, while certain
prescribed pairs of connections are required to be disjoint.
We show that, for any fixed number of terminals, this problem can be solved
in polynomially bounded time for planar directed graphs.
It is based on combinatorial group theory.
We also discuss related problems. No specific preknowledge is assumed.
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