Invited Speakers

(In alphabetical order)

Baruch Barzel

Bar-Ilan University

Network GPS: A Perturbative Theory of Network Dynamics

Abstract: Universal network characteristics, such as the scale-free degree distribution and the small world phenomena, are the bread and butter of network science. But how do we translate such topological findings into an understanding of the system's dynamic behavior: for instance, how does the small world structure impact the patterns of flow in the system? Or how does the presence of hubs affect the system's dynamic timescales? In essence, whether it's communicable diseases, genetic regulation, or the spread of failures in an infrastructure network, these questions touch on the patterns of information spread in the network. It all begins with a local perturbation, such as a sudden disease outbreak or a local power failure, which then propagates to impact all other nodes. The challenge is that the resulting spatio-temporal propagation patterns are diverse and unpredictable - indeed, a zoo of spreading patterns in multiple diverging scales - that seem to be only loosely connected to the network topology. We show that we can tame this zoo by exposing a systematic translation of topological elements into their dynamic outcome, allowing us to navigate the network, and, most importantly, to expose a deep universality behind the seemingly diverse dynamics.

Ginestra Bianconi

Queen Mary University of London

The dynamics of higher-order networks: the effect of topology and triadic interactions

Abstract: Higher-order networks capture the interactions among two or more nodes in complex systems ranging from the brain, to chemical reaction networks and ecosystems. Here we show that higher-order interactions are responsible for new dynamical processes that cannot be observed in pairwise networks.
We will cover how topology is key to define synchronization of topological signals, i.e. dynamical signals defined not only on nodes but also on links, triangles and higher-dimensional simplices in simplicial complexes. Interesting topological synchronization dictated by the Dirac operator can lead to the spontaneous emergence of a rhythmic phase where the synchronization order parameter displays low frequency oscillations which might shed light on possible topological mechanisms for the emergence of brain rhythms.
We will also reveal how triadic interactions can turn percolation into a fully-fledged dynamical process in which nodes can turn on and off intermittently in a periodic fashion or even chaotically leading to period doubling and a route to chaos of the percolation order parameter.

Jianxi Gao

Rensselaer Polytechnic Institute

Resilience and tipping points of complex systems

Abstract: This talk focuses on understanding, predicting, and ultimately controlling real-world complex systems in an ever-changing world facing the global challenges of climate change, weather extremes, and other natural and human-induced disasters. I will present our recent works in the field of network science and complex systems about the dimension reduction approach for network resilience.
(i) I will briefly introduce our dimension reduction approach to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive an effective one-dimensional dynamic that accurately predicts the system's resilience.
(ii) We develop a rescaling factor for recovery rates that places various systems with entirely different dynamical parameters, network structure, and state perturbations on the same scale. More importantly, it compares distances to tipping points across multiple systems based on the limited data of abundance and topology.
(iii) Finally, we show how to restore failed systems through microscopic interventions and system noises.

Ágnes Horvát

Northwestern University

Networks in online scholarly communication


Sarika Jalan

Indian Institute of Technology Indore

Coupled Kuramoto Oscillators on Simplicial Complexes

Abstract: For decades, consideration of the pairwise interaction between different networked dynamical units has been at the forefront to capture the underlying dynamics of various physical and biological complex systems. However, it has been sufficiently emphasised that complex systems such as brains and social interaction networks have the underlying topology of higher-order connections, which can be framed using simplicial complexes. Furthermore, adaptation is at the backbone of the construction and functioning of many physical and biological complex systems. To our knowledge, all the investigations pertaining to the adaptation of network structure and its interplay with the dynamical evolution are restricted to pair-wise interactions. Breaking away from this traditional approach, we consider adaptive higher-order coupling interactions among the oscillators. We develop a rigorous mean-field analysis for pure higher-order simplicial complexes experiencing adaptation. These analytics successfully explain the dependence of the onset points of first-order desynchronization on the Hebbian learning parameters.

The results advance the fundamental research on coupled dynamics on simplicial complexes which have recently seen a spurt of the activities, and would also be important for comprehending dynamical behaviours of a range of real-world systems, such as the Brain and social networks, having underlying higher-order interactions which also evolve with time.

Caterina La Porta

University of Milan

Artificial intelligence in breast cancer diagnostics

Abstract: The new frontier of research is to combine wet lab research with predictive models and artificial intelligence. Given my long-standing experience in the field, I will discuss the progress in tumor predictions enabled using network based methods, including our recent results in this filed with the development of an innovative platform for precision medicine for breast cancer, Ariadne.

Naoki Masuda

University at Buffalo

Recurrence view of temporal network data: System-state dynamics, recurrence plot, and embedding

Abstract: Recurrence of a time series refers to the phenomenon in which the value of the observable comes back to a neighborhood of its past value, and time series analysis often benefits from this concept (e.g., hidden Markov model, nonlinear time series prediction). Such a recurrence point of view may be also useful for understanding temporal (i.e., time-varying) network data. I present the following three recurrence-based approaches to temporal network data: (i) a method to estimate dynamics of a discrete latent state summarizing the network at each point of time, (ii) recurrence plot and recurrence quantification analysis, which was originally proposed as a nonlinear time series analysis methods, and (iii) temporal network embedding, which maps out the given temporal network data into a trajectory of a single point in a low-dimensional embedding space (differently from a more common node embedding of networks). I discuss algorithms, theoretical underpinnings, and applications of these methods.

Adilson E. Motter

Northwestern University

Complex Contagion: Unfolding and Control

Abstract: Network cascades are a pervasive phenomenon, with examples ranging from supply chain disruptions, traffic congestions, power outages, and default contagion in financial networks to the spread of misinformation. Such cascades propagate through underlying networks, but in contrast with their epidemic counterparts, they constitute a form of “complex contagion,” in the sense that the “infection” of a new component may require exposure to multiple infected components. Compared to other network spreading processes, cascades may be harder to trigger but also harder to mitigate. In this talk, I will discuss unique ways in which cascades evade mitigation and then present effective mitigation strategies that avoid evasion. The conclusions are relevant in face of the plethora of ripple effects caused by the ongoing pandemic on a multitude of local and global networks.

Juyong Park

Korea Advanced Institute of Science and Technology

Creativity and Networks

Abstract: Recent advances in the quantitative, computational methodology for the modeling and analysis of heterogeneous large-scale data are leading to new opportunities for understanding human behaviors and faculties, including creativity that drives creative enterprises such as science. While innovation is crucial for novel and influential achievements, quantifying these qualities in creative works remains a challenge. Here we present an information-theoretic framework for computing the novelty and influence of creative works based on their generation probabilities reflecting the degree of uniqueness of their elements in comparison with other works. Applying the formalism to a high-quality, large-scale data set of classical piano compositions–works of significant scientific and intellectual value–spanning several centuries of musical history, represented as symbolic progressions of chords, we find that the enterprise’s developmental history can be characterised as a dynamic process composed of the emergence of dominant, paradigmatic creative styles that define distinct historical periods. We also discuss more recent development in the understanding of network-based creativity.

Jie Tang

Tsinghua University

WuDao: Pretrain the World

Abstract: Large-scale pretrained models on web texts have substantially advanced the state of the art in various AI tasks, such as natural language understanding and text generation, and image processing, multimodal modeling. The downstream task performances have also constantly increased in the past few years. In this talk, I will first go through three families: augoregressive models (e.g., GPT), autoencoding models (e.g., BERT), and encoder-decoder models. Then, I will introduce China’s first homegrown super-scale intelligent model system, with the goal of building an ultra-large-scale cognitive-oriented pretraining model to focus on essential problems in general artificial intelligence from a cognitive perspective. In particular, as an example, I will elaborate a novel pretraining framework GLM (General Language Model) to address this challenge. GLM has three major benefits: (1) it performs well on classification, unconditional generation, and conditional generation tasks with one single pretrained model; (2) it outperforms BERT-like models on classification due to improved pretrain-finetune consistency; (3) it naturally handles variable-length blank filling which is crucial for many downstream tasks. Empirically, GLM substantially outperforms BERT on the SuperGLUE natural language understanding benchmark with the same amount of pre-training data.

Marc Timme

Technical University of Dresden

Nonequilibrium Network Dynamics


Fernanda Valdovinos

University of California Davis

How do ecological networks respond to global change?

Abstract: Plant-animal mutualistic networks sustain terrestrial biodiversity and human food security. Environmental changes threaten these networks underscoring the urgency for developing predictive theories on the networks’ responses to perturbations. This talk will present research conducted by my group and collaborators that seeks to understand the dynamics of ecological networks to inform predictions on their responses to environmental changes. I will present theoretical work predicting: i) foraging preferences of pollinators measured in the field, ii) invasion success and impacts on natives, iii) effects of phenology on pollinator specialization, and iii) interaction rewiring as response to a severe drought. I will end my talk by calling for a better integration of empirical and theoretical approaches to study the response of ecological networks to environmental changes.