Network Science and AI have traditionally approached complex data, but using different methodologies — the former focusing on structural properties and mathematical models, the latter on generalized function approximation and statistical inference. This paradigm is shifting with the maturation of Graph Representation Learning, in which network components (nodes, edges, or entire graphs) are transformed into a continuous vector space. This approach makes it much easier to extend these components with any attributes (e.g. weighted graphs). Learning representations also poses several problems for dynamic structures, such as temporal graphs, which require regularized alignment. Simultaneously, there are two relatively new but interlinked domains in AI: foundation models and generative models (GenAI). By applying these concepts to graph structures, we obtain Graph Foundation Models (GFMs) that provide scalable, general-purpose intelligence for structured data, enabling broad transfer across graph-centric tasks and domains. Foundation models are pre-trained on a large number of graphs (or subgraphs), so they are capable of capturing general knowledge across domains, which is then exploited for simple downstream tasks or generative tasks. Appropriate learning and inference stages will be enumerated. GFM models can also be integrated with natural language, making it easier to generate or even improve graph structures with natural-language prompts. The lecture will also cover some additional topics related to rational and non-rational learning, cognitive science, and the contribution of network science to the problem of AI model structures.

Bio: Przemysław (Przemek) Kazienko, Ph.D. is a full professor and leader of three research groups: Impact AI (AI impact on humans, social influence), HumaNLP (human-centred NLP, LLMs), and Emognition (affective computing, fundamental models for physiological signals) at Wroclaw Tech (Wroclaw University of Science and Technology), Poland. He has authored over 300 research papers, including 60+ in journals with impact factor related to social/complex network analysis, complex networks, personalization and subjective tasks in NLP, Large Language Models (LLMs), self-learning LLMs, hallucination, ethics and responsibility in AI, affective computing and emotion recognition, deep machine learning, sentiment analysis, collaborative systems, recommender systems, information retrieval, data security, and many others. He delivered over 40 keynote and invited talks to international audiences and served as co-chair for more than 20 international scientific conferences and workshops. He initiated and led over 50 projects, including large European ones, primarily in collaboration with companies with a total local budget exceeding €10M. He is an IEEE Senior Member, a member of the Polish Committee for Standardization in AI, and the Ethics Committee for the LLM development. He has been on the board of Network Science Society for several years.

Web page: https://kazienko.eu/en