Keynote speakers & programme at a glance

Professor Tina Eliassi-Rad (Northeastern University)	Professor Claire Postlethwaite (University of Auckland)	Associate Professor Yukie Sano (University of Tsukuba)
Dr. Jie (Rio) Sun (Huawei Hong Kong Research Center)		Professor Lewis Mitchell (University of Aidelaide)

School headline speaker

	Professor Eduardo Altmann (University of Sydney)

Programme at a glance

Detailed programme as a PDF.

Repository of all submitted contributions on OpenReview

	Tuesday 17		Wednesday 18		Thursday 19		Friday 20
08:00:00	Registration open		Registration open		Registration open		Registration open
9:00-10:15	Meso-scale organization of networks: theory and applications (1/2) – Eduardo Altmann	9:00-10:00	Keynote 1: Tina Eliassi-Rad	9:00-10:00	Keynote 3: Rio Sun	9:00-10:00	Coffee and networking morning
10:15-10:45	Coffee break	10:00-10:30	Coffee break	10:00-10:30	Coffee break	10:00-11:00	Keynote 5: Clare Postlethwaite
10:45-12:00	Meso-scale organization of networks: theory and applications (2/2) – Eduardo Altmann	10:30-12:00	Presentation Session	10:30-12:00	Parallel thematic session	11:00-12:00	Presentation Session
					Discrete network models in biology
					Thematic session: Networks in Science of Science
12:00-1:15	Lunch	12:00-1:15	Lunch	12:00-1:15	Lunch	12:00-1:15	Lunch
1:15-2:30	Indigenous Data Sovereignty – Daniel Wilson	1:15-2:15	Keynote 2: Lewis Mitchell	1:15-2:45	Parallel thematic session	1:15-2:45	Presentation Session
					Algorithmic Fairness in Network Science
					The scales of Movement
2:30-3:45	Gentle Introduction to Deep Graph Learning for Network Scientists – Ingo Sholtes	2:15-3:45	Presentation Session	2:45-3:45	Keynote 4: Yukie Sano
3:45-4:15	Coffee break	3:45-4:15	Coffee break	3:45-4:15	Coffee break	2:45-3:15	Coffee break
4:15-5:30	Ecological Networks: from data reconstruction to dynamical insights – Kate Wootton and Anshuman Swain	4:15-5:30	Spotlight Talks	4:15-5:30	Lightning Talks	3:15-3:30	Closing
5:30-late	Mihi whakatau - official conference welcome for all attendees by mana whenua NetPlace Panel Welcome reception	5:30-7:30	Poster session and reception	7:00-late	Conference dinner

School abstracts

Eduardo Altman
Meso‑scale Organization of Networks: Theory and Applications
Complex networks often exhibit rich meso‑scale organization, characterized by groups of nodes that play distinct structural and functional roles. Identifying and interpreting these meso‑scale structures is crucial for understanding the underlying system behaviour and for uncovering the role of different nodes in the network. These two lectures will discuss different approaches for detecting such structures (e.g., community-detection methods) and will critically compare heuristic techniques with principled statistical and inferential frameworks. Foundational concepts — introduced with code examples available at https://github.com/edugalt/netscix26 — will be complemented by recent applications, including the classification of meso‑scale structures and the use of network-based techniques for topic‑modelling text corpora.

Ingo Sholtes
Gentle Introduction to Deep Graph learning for Network Scientists
Graph Neural Networks (GNNs) have recently become a cornerstone for the application of deep learning to data on complex networks. Different from earlier approaches to apply standard machine learning techniques to complex networks, these models use algorithms like neural message passing and neural backpropagation to automatically learn useful vector space representations of graph-structured data, that allow to address node-, edge- and graph-level learning tasks. Starting from the very basics of machine learning, in this lecture I give a gentle introduction to GNNs that specifically targets researchers from network science. Apart from introducing basic architectures like Graph Convolutional Networks (GCN) from a dynamical systems point of view, we will also discuss recent developments like GNNs for Temporal Graphs, and highlight open challenges in deep graph learning that could benefit from network science methods.

Kate Wootton & Anshuman Swain
Ecological Networks; From data reconstruction to dynamical insights
Ecology is the study of the relation of organisms to each other and to their physical surroundings, and networks have been used in ecology since at least the 1800s when the earliest food webs were developed. Here, we will give a broad overview of the range of ecological networks that exist and some of the insights they have given us. A major challenge underlying the development of ecological networks and the insights we gain from them is data collection, so we will discuss some of the wide range of ways that data for ecological networks are collected, their biases and limitations, and what that might mean for the inferences we can (or can’t) draw. We will delve into dynamics in ecological networks, where they’ve been successful, what they’ve taught us, where they’re limited, and some of the current frontiers being addressed. Finally, we will discuss some of the current challenges and frontiers in ecological networks, including data aggregation, spatial and temporal scale, multilayer networks, determining network completeness and inferring missing data, and social-ecological networks.

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