Network neuroscience investigates brain structure and function by using the tools of network science. In brain network models, structural connections between brain region pairs are modeled from diffusion-weighted imaging data, usually named structural connectome or structural connectivity. Functional connections are modeled from functional magnetic resonance imaging data by measuring temporal statistical dependencies between brain region pairs, usually known as functional connectivity or functional connectome. Examining several types of human brain connectivity data offers new insights on how the integration and segregation of information in the brain relates to human behavior, and how this organization may be altered in neurological diseases and disorders. However, this exploration has so far mainly been constrained to pairwise interactions, e.g. looking at brain networks as a set of dyadic relationships among regions. Beneficial because of its simplicity, this assumption severely limits the investigation of the complex dynamical processes arising from brain networks in health and disease. Here we try to overcome the aforementioned issue by proposing a new approach, rooted in recent advances in topological data analyses and simplicial complexes, and investigate the higher-order structural and functional organization of the brain. The overarching goal of this proposal is to explore macro-scale, time-dependent and disease-specific higher-order processes in human brain networks with the purpose of getting a deeper understanding of scenarios where brain dynamics is impaired or affected, as in the case of complex neurodegenerative diseases such as Alzheimer’s.
The project is administered at EPFL Lausanne.
The HumanE AI Network will leverage the synergies between the involved centers of excellence to develop the scientific foundations and technological breakthroughs needed to shape the AI revolution in a direction that is beneficial to humans on both individual and societal levels and that adheres to European ethical values and social, cultural, legal and political norms. The core challenge is the development of robust, trustworthy AI systems capable of what could be described as “understanding” humans, adapting to complex real-world environments and appropriately interacting in complex social settings. The aim is to facilitate AI systems that enhance human capabilities and empower individuals and society as a whole while respecting human autonomy and self determination. The HumanE AI Net project will bring about the mobilization of a research landscape far beyond direct project funding, involve and engage European industry, reach out to relevant social stakeholders, and create a unique innovation ecosystem that will provide a many-fold return oninvestment for the European economy and society.
There are 53 partner institutions on this project.
DNDS will develop a data-driven modelling framework to emulate the effect of algorithmic bias on the way people interact and share information in online social networks. By systematically analysing many filtering algorithms and diverse social behaviors, DNDS will devise tools to discover bias in algorithms, quantify it, and potentially correct it. Our project has the potential to inform the development of science-based policies to foster a healthy flow of information in our modern digital ecosystems.
SoBigData++ strives to deliver a distributed, Pan-European, multi-disciplinary research infrastructure for big social data analytics, coupled with the consolidation of a cross-disciplinary European research community, aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society.
SoBigData++ is set to advance on such ambitious tasks thanks to SoBigData, the predecessor project that started this construction in 2015. Becoming an advanced community, SoBigData++ will strengthen its tools and services to empower researchers and innovators through a platform for the design and execution of large-scale social mining experiments. It will be open to users with diverse background, accessible on project cloud (aligned with EOSC) and also exploiting supercomputing facilities. Pushing the FAIR principles further, SoBigData++ will render social mining experiments more easily designed, adjusted and repeatable by domain experts that are not data scientists.
SoBigData++ will move forward from a starting community of pioneers to a wide and diverse scientific movement, capable of empowering the next generation of responsible social data scientists, engaged in the grand societal challenges laid out in its exploratories: Societal Debates and Online Misinformation, Sustainable Cities for Citizens, Demography, Economics & Finance 2.0, Migration Studies, Sport Data Science, Social Impact of Artificial Intelligence and Explainable Machine Learning.
SoBigData++ will advance from the awareness of ethical and legal challenges to concrete tools that operationalise ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency and pluralism. SoBigData++ will deliver an accelerator of data-driven innovation that facilitates the collaboration with industry to develop joint pilot projects, and will consolidate an RI ready for the ESFRI Roadmap and sustained by a SoBigData Association.
Searching on the internet has got a considerable part of our everyday activity and studying the laws of this process with the goal of possible improvement of strategies and data structures is a real challenge. The approach of this project is not algorithmic; instead, we plan to understand how navigation strategies are influenced by cognitive and social biases, by the learning processes of individuals, their characteristics like age, gender, and cultural background. We will study the dynamics of the searches as recorded by clickstreams, which will be a tool to map out the complex information landscape. This landscape can be represented by a weighted network and it evolves during the learning process as exploration proceeds. A further interesting question will be to explore if the content, like Wikipedia categories of the target of search influence the strategy. We plan to study with similar tools the related problem of search for information, which, in contrast to navigation, has no a priory given target. An example can be the acquisition of news, where the search strategy can be a source of filter bubbles known as one of the origins of increasing political polarisation. We are planning to analyze big datasets from Wikipedia related games (Wiki Game and Wikispeedia), Wikipedia clickstream data, online social network data, and Mechanical Turk based experiments.
The project is administered at RWTH Aachen University.
Networks define our life. They are essential to biology, communications, social and economic systems, they influence virtually all areas of science and technology. But their workings are not fully understood. Laszlo Lovasz from the Hungarian Academy of Sciences and Jaroslav Nesetril from Charles University in Prague, renowned mathematicians specialising in graph theory, and Laszlo Barabasi, a leading expert in network science based at the Central European University in Budapest, aim to build a mathematically sound theory of dynamical networks. They want to transform our understanding of complex systems and prepare the ground for applications in multiple disciplines.
Both graph theory in mathematics and the study of networks have made major conceptual advances in the past decade. However, the research communities working in these two disciplines had little conversation between each other, and that limited our insight. The research funded with an ERC Synergy Grant can potentially change it, constructing a coherent theory of dynamical networks, and exploiting its applications and predictive power for various real systems. To enhance the wider impact of the proposed mathematical advances, the principal investigators plan to establish steady links with experts from different domains that encounter and explore networks, from cell biology to brain science and transportation and communication networks, inspiring with novel questions and helping the application of our advances in these domains.