Invited Speakers

István Kovács

Bio: Professor Kovács is working on bridging the gap between structure and function in complex systems. His group is developing novel methodologies to predict the emerging structural and functional patterns in a broad spectrum of problems ranging from systems biology to quantum physics, in close collaboration with experimental groups.

Website: https://sites.northwestern.edu/kovacslab

Talk title: Understanding social balance through maximum entropy models

Abstract: Social networks inherently exhibit complex relationships that can be positive or negative, as well as directional. Understanding balance in these networks is crucial for unraveling social dynamics, yet previous approaches struggled to incorporate all the relevant constraints as well as the directed nature of the interactions. For example, even if an undirected network exhibits strong balance by construction, previous null models can fail to identify it. In this talk, I present a comprehensive framework for understanding balance in signed networks, directed or undirected, advancing our understanding of complex social systems and their dynamics. Balance is indicated by the enrichment of higher-order patterns like triads compared to an adequate null model, where the network is randomized with some key aspects being preserved. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose null models based on the maximum-entropy principle that reveals consistent patterns of balance across large-scale social networks. We also consider directed generalizations of balance theory and find that the observed patterns are well aligned with two proposed directed notions of strong balance. We close by discussing potential wiring mechanisms behind the observed signed patterns and outline some of the key open questions.

Alec Kirkley

Bio: Professor Alec Kirkley is a physicist interested in the theory of complex networks, statistical physics, as well as their applications to urban and social systems. The mathematical and computational methods he develops in his research draw on ideas from a range of disciplines including statistical physics, information theory, Bayesian inference, scientific computing, and machine learning. He received his PhD in Physics at the University of Michigan in 2021 under the supervision of Mark Newman and joined HKU as an Assistant Professor in 2022. His main research interests lie in developing principled unsupervised learning methods for noisy network data and improving the efficiency and interpretability of statistical inference methods for networks. He also adapts and applies these techniques to uncover new insights about the structure and dynamics of urban mobility as well as the underlying topology of geographical data.

Website: https://aleckirkley.com/

Talk title: Defining Balance in Signed Networks

Abstract: Signed networks provide a fundamental representation of relational data that captures the qualitative distinction between amicable and antagonistic relationships, offering a powerful lens for understanding the emergence of conflict and cooperation in social, political, and biological systems. Central to this framework is the theory of structural balance, which posits that certain patterns of signs, such as “the enemy of my enemy is my friend”, should occur more frequently than others, reflecting an inherent tendency toward stability in these systems. Despite decades of research devoted to formulating and testing theories of balance, the field remains fragmented, with numerous competing definitions and no consensus on how balance should properly be measured or even what constitutes a balanced state. This lack of agreement is a theoretical concern but also has practical implications, as the structural regularities that push networks toward balanced configurations can be leveraged to predict missing data, identify anomalous or unstable relationships, and forecast the evolution of social ties over time. In our 2019 paper “Balance in signed networks”, we aimed to tackle these foundational issues by proposing two new measures of partial structural balance grounded in the strong and weak theories of balance, systematically evaluating their performance against existing measures across multiple tasks and datasets. Our results showed that all measures detected significant levels of balance across the networks studied, and that our proposed measures achieve prediction accuracies substantially above random baselines, validating their efficiency for downstream tasks. However, looking forward, I argue that the path toward resolving the fragmentation in balance theory may lie in adopting a principled information-theoretic perspective: the most robust and unifying definition of balance may be the one that allows for the most efficient compression of a network’s structure, reframing the search for balance as a search for the fundamental regularities that best explain observed patterns of amity and enmity. This compression-based approach promises to provide a principled foundation for signed network analysis, moving beyond heuristic formulations toward a unified theory of structural balance.

Rui-Sheng Wang

Bio: Website: https://connects.catalyst.harvard.edu/Profiles/display/Person/120387

Talk title: Boolean network modeling in systems pharmacology: understanding of the molecular mechanism of drug effects

Abstract: Systems pharmacology seeks to understand drug effects beyond single targets by examining their impact on complex cellular networks. However, the lack of detailed kinetic parameters often limits the use of traditional quantitative models. In this talk, I will highlight Boolean network modeling as effective approaches for integrating qualitative biological knowledge into predictive frameworks of cellular regulation in the context of system pharmacology. As a case study, we applied a Boolean logical modeling framework to investigate the mechanisms of action of statins in combination with ezetimibe using transcriptomic data from hepatocyte-like cells and human liver biopsies. By linking drug-induced gene expression patterns to Boolean models and mapping downstream effects onto the human protein–protein interactome, we identified both known and novel combinatorial mechanisms and pathways. Together, this talk will illustrate how Boolean modeling integrated with transcriptomics and network biology can provide mechanistic insights into drug action and combination therapies, offering a scalable approach for deciphering systems-level drug mechanisms.