Fernando Diaz-Diaz, Elena Candellone, Miguel A. Gonzalez-Casado, Emma Fraxanet, Antoine Vendeville, Irene Ferri, Andreia Sofia Teixeira

Signed networks provide a principled framework for representing systems in which interactions are not merely present or absent but qualitatively distinct: friendly or antagonistic, supportive or conflicting, excitatory or inhibitory. This polarity reshapes how we think about structure and dynamics in complex systems: a negative tie is not simply a missing positive one but a constraint that generates tension, and possibly asymmetry. Across disciplines, from sociology to neuroscience and machine learning, signed networks provide a shared language to formalise duality, balance, and opposition as integral components of system behaviour. This review provides a comprehensive and foundational summary of signed network theory. It formalises the mathematical principles of signed graphs and surveys signed-network-specific measures, including signed degree distributions, clustering, centralities, motifs, and Laplacians. It revisits balance theory, tracing its cognitive and structural formulations and their connections to frustration. Structural aspects of signed networks are examined, analysing key topics such as null models, node embeddings, sign prediction, and community detection. Subsequent sections address dynamical processes on and of signed networks, such as opinion dynamics, contagion models, and data-driven approaches for studying evolving networks. Practical challenges in constructing, inferring and validating signed data from real-world systems are also highlighted, and we offer an overview of currently available datasets. We also address common pitfalls and challenges that arise when modelling or analysing signed data. Overall, this review integrates theoretical foundations, methodological approaches, and cross-domain examples, providing a structured entry point and a reference framework for researchers interested in the study of signed networks in complex systems.

Read the full article at: arxiv.org

Paola Di Maio

Artificial Intelligence and Digital Forensics

The intersection of artificial intelligence (AI) and digital forensics (DF) is becoming increasingly complex, ubiquitous, and pervasive, with overlapping techniques and technologies being adopted in all types of scientific and technical inquiry. Despite incredible advances, forensic sciences are not exempt from errors and remain vulnerable to fallibility. To mitigate the limitations of errors in DF, the systemic complexity is identified and addressed with the adoption of human-readable artifacts and open standards. A DF AI model schema based on the state of the art is outlined.

Read the full article at: www.taylorfrancis.com

Luiz H. A. Monteiro

Complexities 2025, 1(1), 2

Defining a complex system and evaluating its complexity typically requires an interdisciplinary approach, integrating information theory, signal processing techniques, principles of dynamical systems, algorithm length analysis, and network science. This overview presents the main characteristics of complex systems and outlines several metrics commonly used to quantify their complexity. Simple examples are provided to illustrate the key concepts. Speculative ideas regarding these topics are also discussed here.

Read the full article at: www.mdpi.com

Giulio Tononi, Melanie Boly

This overview of integrated information theory (IIT) emphasizes IIT’s “consciousness-first” approach to what exists. Consciousness demonstrates to each of us that something exists–experience–and reveals its essential properties–the axioms of phenomenal existence. IIT formulates these properties operationally, yielding the postulates of physical existence. To exist intrinsically or absolutely, an entity must have cause-effect power upon itself, in a specific, unitary, definite and structured manner. IIT’s explanatory identity claims that an entity’s cause-effect structure accounts for all properties of an experience–essential and accidental–with no additional ingredients. These include the feeling of spatial extendedness, temporal flow, of objects binding general concepts with particular configurations of features, and of qualia such as colors and sounds. IIT’s intrinsic ontology has implications for understanding meaning, perception, and free will, for assessing consciousness in patients, infants, other species, and artifacts, and for reassessing our place in nature.

Read the full article at: arxiv.org

Renli Wu, Christopher Esposito, and James Evans

PNAS 122 (44) e2414893122

China’s emergence as one of the world’s top producers of high-quality science raises critical questions about its trajectory toward achieving scientific leadership. Traditional methods for evaluating the power of national scientific ecosystems, however, often overlook the nuances of a country’s global influence. In this perspective, we introduce a framework that highlights shifting power dynamics in international scientific collaborations, focusing on whether leadership positions in international scientific teams are moving from one country to another. Using rich sociological data from nearly 6 million scientific publications, we document a marked shift in team leadership from Western countries to China. In particular, the share of team leaders involved in U.S-China scientific collaborations that were affiliated with Chinese institutions grew from 30% of the total in 2010 to 45% in 2023. We further explore the implications of China’s rise by forecasting when Chinese scientists are projected to achieve parity in leadership vis-à-vis the United States, including in 11 critical technology areas that are focal points of technological development, and by analyzing how a potential decoupling of U.S.-Chinese science might affect Chinese scientific leadership. We conclude by considering the impacts of China’s growing investments in the training of young scientists in countries participating in the Belt and Road Initiative.

Read the full article at: www.pnas.org