The School of the NetSci 2022 conference is inspired by two topics, higher-order interactions, and statistical inference.

Higher-order interactions is an emerging field calling for the generalization of network science principles beyond pairwise interactions. By attending the School, you will have a chance to learn about the most recent developments in the higher-order interactions branch of network science, including generalized diffusive processes, synchronization, higher-order motifs, and communities.

Statistical inference on/of networks has been at the heart of all network science branches. At NetSci2022, we highlight recent developments in statistical inference from several angles, from theory to applications. Following school lectures will enrich your arsenal of statistical inference tools with spectral, information-theoretic, and machine-learning methods.

The time shown in the list is the Greenwich Mean Time (GMT+0:00). Please convert it to the time zone where you are going to attend the event.

Some common locations and corresponding current time:

- Greenwich Mean Time (GMT+0:00): 2024/10/6 20:17:50
- Shanghai Time (GMT+8:00): 2024/10/7 4:17:50
- US Eastern Daylight Time (GMT-04:00): 2024/10/6 16:17:50
- Central European Time (GMT+02:00): 2024/10/6 22:17:50

Wednesday, July 20 | |

12:00 ～ 13:45 | Renaud
Lambiotte "Consensus and random walks on higher-order networks" |

13:45 ～ 15:30 | Yuanzhao
Zhang "Synchronization in networks with higher-order interactions" |

Thursday, July 21 | |

12:00 ～ 13:45 | Jan
Nagler "Fundamentals of Networked Dynamical Interactions: A journey from pairwise to higher-order" |

13:45 ～ 15:30 | Federico
Battiston "The organization of higher-order interactions in real-world systems" |

Friday, July 22 | |

12:00 ～ 13:45 |
Jie Sun "Data Science for Network Science: A Dynamical and Complex Systems Perspective" |

13:45 ～ 15:30 | Yulia R. Gel "Topological Data Analysis on Complex Networks: Blockchain Transaction Graphs and Beyond" |

15:30 ～ 17:15 | Cristopher Moore "Statistical inference and community detection in networks" |

Saturday, July 23 | |

12:00 ～ 13:45 | Yanqing
Hu "Applications of Coding on Complex Networks" |

Cancelled | Pan
Li "Graph neural networks --- its power, its limit and recent applications" |

July 20, 12.00 to 13.45 GMT

**Abstract: **In the last years, there have been a number of works on the limits of graph-based models to represent interacting systems. Different directions have been proposed in order to enrich the network formalism, leading to the emerging field of higher-order networks. In this lecture, I will focus on random walk and diffusive processes, and discuss their behaviour for different types of higher-order networks, including temporal networks, non-Markovian networks and hypergraphs. I will also discuss the possibility to use the resulting diffusive processes in order to extract information from the data, for instance via community detection.

July 20, 13.45 to 15.30 GMT

**Abstract: **Higher-order interactions, through which three or more entities interact simultaneously, are important in many complex systems and have generated great interest in the network science community. In this lecture, I will sample a few rapidly developing directions at the interface between nonlinear dynamics and higher-order networks. A key theme of the lecture will be the effects of nonpairwise interactions on synchronization. For example, when do nonpairwise interactions promote synchronization? What about more complex synchronization patterns such as chimera states? And how do we analyze these synchronization patterns in the presence of nonpairwise interactions?

July 21, 12.00 to 13.45 GMT

**Abstract: **Do higher-order interactions really matter? If yes, when, how and why?
I plan to discuss networked examples in signal propagation, percolation,
machine learning and causal modeling, with excursions to cosmology, non-self-averaging systems and strong
correlates. I will decorate my lecture with related own work on networked dynamical systems, published and unpublished.
I begin simple using an electronic black board and ask plain, yet fundamental questions. I hope for an interactive lecture.

July 21, 13.45 to 15.30 GMT

**Abstract: **The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, from human communications to chemical reactions and ecological systems, interactions can often occur in groups of three or more nodes and cannot be described simply in terms of dyads. Until recently little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can enhance our modeling capacities and help us understand and predict their dynamical behavior. In this lecture I will navigate a number of ways to untangle the higher-order organization of real-world systems, from higher-order motifs and filtering procedures to temporality, community structure and signal processing.

July 22, 12.00 to 13.45 GMT

**Abstract: **From telecommunication systems to power grids and robotics, complexity is an integral signature in today’s society. Coping with those ever-complex systems call for new ideas and methodology. We start by presenting common challenges in the modeling of complex systems and discussing how data-driven techniques can be useful in mitigating some of the difficulties. Some basic theory from matrices, linear systems, nonlinear dynamics and stochastic processes together with algorithms will be discussed over the talk. We will also survey recent progress on open problems in causal inference, data-driven modeling and optimization of complex networks.

July 22, 13.45 to 15.30 GMT

**Abstract: **"Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been
seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-
tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to
construct a transaction graph and to assess not only its organization but to glean relationships between transaction
graph properties, crypto price dynamics as well as illegal and illicit activities such as emerging ransomware.
In this talk we discuss horizons and limitations of what new can be learned from topology and geometry of cryptocurrency
transaction graphs whose even global network properties remain scarcely explored. By introducing novel tools based
on topological data analysis (TDA), functional data depth, and geometric deep learning, we show that even some subtler patterns in blockchain transaction graphs can provide critical insights for money laundering tracking, price analytics, and
market sentiment assessment. If time permits, we also discuss how the TDA concepts can be used
to shed new light onto hidden mechanisms behind organization of complex networks, in conjunction with
clustering, node classification, and link prediction in social media, cyber-physical systems, and
climate-induced insurance analytics"

July 22, 15.30 to 17.15 GMT

**Abstract: **"Statistical inference is the art of finding patterns in noisy data. Many modern inference problems are high-dimensional: namely, where both our data and the model we want to fit to it are very large. This includes community detection in networks, topic models in text analysis, and so on.
I’ll give an introduction to Bayesian approaches to these problems, where we assume the data was produced by a generative model. I’ll discuss approximate Bayesian methods like belief propagation and expectation maximization. I’ll also discuss connections between these algorithms and spectral approaches. Along the way, I’ll draw analogies with statistical physics, including how free energy barriers can make inference problems hard to solve."

July 23, 12.00 to 13.45 GMT

**Abstract: **In this lecture I will introduce the applications of coding method on complex networks. Specifically, in the first hour, I will establish the relationship between the shortest compressed length of a complex network and its structure predictability. In the second hour, I will show how to identify community structure by encoding the paths of random walks.

July 23,cancelled

**Abstract: **In this talk, we are to introduce graph neural networks for modern learning problems on graphs & networks. We will introduce their basic working mechanism and some important applications. Then, we will introduce a bit more in-depth topic on the power of graph neural networks, particularly on what they can do, what they cannot do, and how to improve their power. If times are allowed, we will introduce some fancy topics regarding graph neural networks, e.g., how to use it to discover patterns in graphs or networks for certain prediction tasks.

Delft University of Technology

Pengcheng Laborabory

Fudan University

Supported by

Sponsored by IOP Publishing

Shanghai Local Time：2024/10/7 4:17:50

Greenwich Mean Time: 2024/10/6 20:17:50

Connect us: NetSci2022@126.com

© CopyRight 2021-2022 Complex Network and Control Lab of Shanghai Jiao Tong University. All rights reserved.

沪ICP备18023304号-3

Greenwich Mean Time: 2024/10/6 20:17:50

Connect us: NetSci2022@126.com

© CopyRight 2021-2022 Complex Network and Control Lab of Shanghai Jiao Tong University. All rights reserved.

沪ICP备18023304号-3