Invited speakers

Niloy Ganguly, IIT Kharagpur, IN
Theo Geisel, MPI Göttingen, GE
Pablo Jensen, ENS Lyon, FR
Annick Lesne, CNRS, LPTMC (Paris) & IGMM (Montpellier), FR
Roeland Merks, Leiden University, NL
Johannes Müller, TU München, GE
Tim Otto Roth, Cologne, GE
Marc Timme, TU Dresden, GE

Titles and abstracts

NeVAE: A Deep Generative Model for Molecular Graphs

Niloy GangulyIIT Kharagpur, India

Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics—their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we propose NeVAE, a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. In addition, by using masking, the decoder is able to guarantee a set of valid properties in the generated molecules. Experiments reveal that our model can discover plausible, diverse and novel molecules more effectively than several state of the art methods. Moreover, by utilizing Bayesian optimization over the continuous latent representation of molecules our model finds, we can also find molecules that maximize certain desirable properties more effectively than alternatives.

The politics of social models

Pablo Jensen ,Institut Rhônalpin des Systèmes Complexes, IXXI, Lyon, France and Université de Lyon, Laboratoire de Physique ENS Lyon and CNRS, Lyon, France

I discuss several examples of simple social models put forward by physicists and discuss their interest. I argue that while they may be conceptually useful to correct our intuitive models of social mechanisms, their relevance for real social systems is moot. What is more, since physicists have always needed to ’tame’ the world inside laboratories to make their models relevant, I suggest that social modeling might be linked to human taming, a smashing political project.

Topological determinants of excitation propagation and self-sustained activity on excitable networks

Annick Lesne, CNRS, LPTMC (Paris) & IGMM (Montpellier), France.
Work done with M.-T. Hütt (Jacobs Univ., Bremen, Germany) and C. Hilgetag (Hamburg Univ., Germany & Boston Univ., USA)

Discrete models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering.  In particular, we have investigated how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe  that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology.  Our findings are interpreted in the context of  self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

Population Genetics and Democratic Elections

Johannes Müller, TU München, Germany. Joint work with Volker Hösel, TUM, and Aurelien Tellier, TUM.

In recent years it became clear that the voter model can be used to model democratic elections. One point needs to be adapted: The Voter model on a connected, finite graph tends in the long run to an absorbing state, where only one opinion/party is left. Therefore, different forms of the noisy voter model have been introduced [1,2], where individuals choose with a certain probability one of the given opinions, without interaction with neighbours.

We propose a slightly different approach, based on the infinite allele model, that is well known in population genetics. With a certain probability, new groups are created, and old groups disappear. We show that this model (with slight adaptations to avoid opinions/parties with only few supporters) on a full graph has properties that are in line with election data from the US, Netherlands, France and Germany [3].
In the present talk we investigate the importance of the graph structure, with the aim to better understand mechanisms behind the variance in election data.
1. Granovsky B.L., Madras N. (1995) The noisy voter model. Stoch. Process. Their. Appl. , 55:2343
2. Braha D., de Aguiar M. (2017) Voting contagion: Modeling and analysis of a century of U.S. presidential elections. PLoS ONE, 12:e0177970
3. Hösel, V., Müller, J. Tellier, A. (2019) Universality of neutral models: decision process in politics. Palgrave Comm., in press.

From Pixelsex to Mathematical Socialism – confronting cellular automata with real (artistic) live

Tim Otto Roth, Cologne, GE

Future Mobility  : Self-Organization, Inefficiencies and Paradoxa

Marc Timme, Chair for Network Dynamics, TU Dresden

Human mobility
together with human-centric transport, the transport of the goods
humans produce, use and discard
fundamentally underlies all aspects of our modern society. How we
work, how we spend our free time, how we consume goods and services,
how we use and need energy, how we protect our health and ensure
environmental sustainability. Besides the large number of challenges
existing today, including to contain climate change, to avoid traffic
grid locks and to reduce emissions, a vast range of
technological innovations impinges on mobility systems today. I
highlight how questions on human mobility open up a fascinating
research field on self-organization processes described by statistical
physics and nonlinear dynamics of coupled multi-dimensional systems.
Models, simulations (and real world data) here bridge the regimes
between few, discrete entities and infinite numbers of entities
characterized, e.g., by continuous flows. First, I highlight why and
how hysteresis persistently causes major inefficiencies across
ride-hauling (taxis, ride sharing etc.)systems. Second, I illustrate
how ride pooling may enable smoother door-to-door service without the
need of owning a private car, also illustrating a recent pilot project
we organized. Finally, I point out how tech-enabled routing systems
may be optimized not for fastest individual route but for overall
effectiveness if the routes of many travelers collectively minimize
the total time wasted. I am happy to discuss the long list of open
questions on future mobility.
This is work with Malte Schroeder, Philip Marszal, David Storch and others.