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VERSION:2.0
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BEGIN:VEVENT
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CATEGORIES:D21 227,Community Detection,Fairness,Multilayer Networks
DESCRIPTION:MOUFLON is a fairness-aware\, modularity-based community detection method that allows adjusting the importance of partition quality over fairness outcomes. It uses a novel proportional balance fairness metric\, providing consistent and comparable fairness scores across multi-group and imbalanced network settings. The talk will cover conceptual details of the framework as well as its recently elaborated extension to multilayer networks.\n\nGeorgios is a PhD candidate in Computer Science at Uppsala University\, Sweden\, and a member of the InfoLab research group. His research focuses on complex network data engineering. He is particularly interested in scalable and fairness-aware methods for managing and mining feature-rich networks\, and their applications within computational social science.\n\nOur guest will present remotely. The on-site meeting will take place in building D-21\, room 227 at the WUST campus.\n\n
DTEND;TZID=Europe/Warsaw:20260527T104500
DTSTAMP:20260419T052930Z
DTSTART;TZID=Europe/Warsaw:20260527T091500
GEO:51.1098702;17.0584436
LOCATION:11a plac Grunwaldzki 11a plac Grunwaldzki 50-377 Wrocław województwo dolnośląskie Polska
ORGANIZER:Network Science Lab
STATUS:CONFIRMED
SUMMARY:NSL Seminar – Georgios Panayiotou
UID:a989930f-0025-4708-92d1-c77c05154882
URL:https://events.ai.pwr.edu.pl/events/a989930f-0025-4708-92d1-c77c05154882
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CATEGORIES:AI Agents,Gen AI,Claude,D21 227,Chat GPT
DESCRIPTION:This seminar will explore the idea of AI as infrastructure and its implications for software development in research environments. We will discuss how tools such as Claude Code and similar coding-optimised agents can be integrated into research teams and laboratories as part of everyday workflows. In this emerging paradigm\, natural language increasingly becomes the primary interface for programming\, and researchers shift from writing code line by line to designing systems\, specifying constraints\, and supervising agentic pipelines. The focus will be on the practical use of AI agents to support experimentation\, prototyping\, and the maintenance of research software. The speaker will share practical experience demonstrating substantial productivity gains\, including cases where output increased severalfold without directly writing code.\n\nDamian Brzoza is an MLOps Engineer at Synerise (https://www.synerise.com/)\, where he automates deep learning pipelines from data acquisition through training to production inference. Outside of work\, he is a devoted mountain hiker and a fierce Skat player.\n\n
DTEND;TZID=Poland:20260401T104500
DTSTAMP:20260419T052930Z
DTSTART;TZID=Poland:20260401T091500
GEO:51.1098702;17.0584436
LOCATION:11a plac Grunwaldzki 11a plac Grunwaldzki 50-377 Wrocław województwo dolnośląskie Polska
ORGANIZER:Network Science Lab
STATUS:CONFIRMED
SUMMARY:Seminarium NSL – Damian Brzoza, AI as Infrastructure
UID:a111737b-ec53-4ce4-a12f-94b082afcdf9
URL:https://events.ai.pwr.edu.pl/events/a111737b-ec53-4ce4-a12f-94b082afcdf9
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BEGIN:VEVENT
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CATEGORIES:D21 227,Online
DESCRIPTION:Prezentacja Narimene Dakiche z École Nationale Supérieure d'Informatique Algier (online)\n\nTytuł: Communities at Scale: Detection\, Tracking\, and Prediction in Dynamic Social Networks\n\nDynamic social networks challenge standard community detection: partitions can be unstable\, and apparent evolution events may be artifacts of discretization and algorithmic variability. This seminar highlights methods and tools for reliable community evolution analysis\, including sensitivity analysis of timeframe choices\, prediction of evolution using community feature change rates\, tailored network splitting\, and a two-phase\n\ndetection-and-tracking framework (Com_Tracker). It also introduces EPredictor\, an experimental platform for reproducible evaluation\, and optimization-based approaches for community detection\, including extensions to multiplex networks. The seminar concludes with a research agenda linking multilayer community structure to polarization mechanisms in opinion dynamics under Friedkin–Johnsen models.\n\nBio: Narimene Dakiche is an Associate Professor at the École Nationale Supérieure d’Informatique (ESI) in Algiers\, affiliated with the Laboratoire de Méthodes de Conception de Systèmes (LMCS) and the SOPROS research team. She received her PhD in Computer Science from ESI in 2022. Her research lies in network science and social network analysis\, with a focus on community structure in evolving networks\, including community detection\, dynamic community tracking\, and community evolution prediction. Her current interests also include linking multilayer topology to opinion dynamics and polarization.\n\nUwaga: prezentacja będzie zdalna. Można jej wysłuchać zarówno w 227 D21\, jak i podłączyć się bezpośrednio. W takim przypadku prosimy o kontakt.\n\n
DTEND:20260325T091500Z
DTSTAMP:20260419T052930Z
DTSTART:20260325T081500Z
GEO:51.1098702;17.0584436
LOCATION:11a plac Grunwaldzki 11a plac Grunwaldzki 50-377 Wrocław województwo dolnośląskie Polska
ORGANIZER:Network Science Lab
STATUS:CONFIRMED
SUMMARY:Seminarium NSL - Narimene Dakiche, Communities at Scale: Detection, Tracking, and Prediction in Dynamic Social Networks
UID:5fd3e1f4-b2a9-4e2a-bc88-50cbb458d53b
URL:https://events.ai.pwr.edu.pl/events/5fd3e1f4-b2a9-4e2a-bc88-50cbb458d53b
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CATEGORIES:D21 227,Short Cycles,Local Density,Community Detection
DESCRIPTION:Prezentacja gościa z Toronto Metropolitan University.\n\nShort Cycles for Local Density and Community Detection\n\nCommunity detection involves finding dense subsets in a graph\, and is a fundamental problem in unsupervised network science. Previous works have used triangle or clique motifs as evidence that the vertices belong in a community together\, but usually\, there are not enough of these motifs to create a strong signal. In this talk\, we propose cycles instead of cliques as the generalization of triangles\, and discuss their connection to local density\, a method for finding short cycles\, and their application to community detection.\n\nRyan DeWolfe is a student in the MSc Applied Mathematics program at Toronto Metropolitan University\, and achieved his BSc with honours in mathematics from the University of British Columbia in 2024. His work blends ideas from math\, statistics\, and computer science to develop data science methods for network data\, with a focus on principled and efficient algorithms for exploratory data analysis.\n\nhttps://www.torontomu.ca/graphs-group/\n\n
DTEND;TZID=Europe/Warsaw:20260318T104500
DTSTAMP:20260419T052930Z
DTSTART;TZID=Europe/Warsaw:20260318T091500
GEO:51.1098702;17.0584436
LOCATION:11a plac Grunwaldzki 11a plac Grunwaldzki 50-377 Wrocław województwo dolnośląskie Polska
ORGANIZER:Network Science Lab
STATUS:CONFIRMED
SUMMARY:Seminarium NSL - Ryan Dewolfe, Toronto Metropolitan University
UID:3a10d943-6218-4903-92df-884d997c8032
URL:https://events.ai.pwr.edu.pl/events/3a10d943-6218-4903-92df-884d997c8032
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CATEGORIES:C16 D2.3,Obrona doktoratu,Network Science Lab
DESCRIPTION:Obrona doktoratu magistra inżyniera Mateusza Nurka pt. Social Systems as Event Streams: A Cognitive Perspective on Temporal Networks.\n\nNetworks have become a major framework for analyzing connections between people and various social phenomena. Over the years\, researchers have developed different representations of social networks. Among many factors\, time is particularly crucial for understanding the constant changes in our connections with others and in our beliefs and behavior. The growing complexity of methods\, on the one hand\, enabled the development of more advanced tools to analyze social network structures and the processes within them. On the other hand\, it has pushed network research toward highly sophisticated concepts from mathematics and computer science\, while simultaneously detaching models and methods from how humans actually experience the world and engage in social interactions. Therefore\, the goal of this thesis is to study social networks through the lens of cognitive science to understand how we perceive and process different stimuli\, including social interactions\, and to incorporate cognitive theories into temporal network research.\n\nTwo key cognitive theories provide the foundations for this dissertation: Event Segmentation Theory and ACT-R. Event Segmentation Theory explains that incoming information can be treated as an event stream\, which is then cognitively processed. Looking at social interactions from this perspective suggests that streams of events should be viewed as the fundamental concept\, rather than networks themselves. Networks remain a useful abstraction\, but from a cognitive perspective\, we interact directly with events\, not networks. Network representations can support analysis\, but they may also introduce distortions by stepping away from the original streaming form. ACT-R explains how memory imprints are formed\, in other words\, how information from event streams is stored and retrieved in memory. This dissertation demonstrates that these memory mechanisms can be applied to better capture the dynamics of temporal social systems.\n\nI argue that cognitive mechanisms can effectively support current approaches to modeling temporal networks by placing greater emphasis on the importance of events. To demonstrate the generality of the proposed approach\, I apply cognitive-inspired methods to a variety of problems\, including temporal link prediction\, social learning\, opinion formation\, and the prediction of relationship types and personality traits from patterns of communication behavior. In all these problems\, methods supported by human cognition achieve better results than classical approaches\, while also allowing for interpretations within the broader context of the social sciences. All experiments are based on real-world data\, ensuring that the findings remain as close as possible to the actual settings of interactions and phenomena in society.\n\n\n\n
DTEND:20260311T150000Z
DTSTAMP:20260419T052930Z
DTSTART:20260311T130000Z
GEO:51.1093591;17.0599097
LOCATION:7 Zygmunta Janiszewskiego 7 Zygmunta Janiszewskiego 50-372 Wrocław województwo dolnośląskie Polska
ORGANIZER:Network Science Lab
STATUS:CONFIRMED
SUMMARY:Obrona doktoratu - Mateusz Nurek, Social Systems as Event Streams: A Cognitive Perspective on Temporal Networks
UID:0f09e176-d665-460f-a042-52d30b401ed6
URL:https://events.ai.pwr.edu.pl/events/0f09e176-d665-460f-a042-52d30b401ed6
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CATEGORIES:D21 227,Reading Club
DESCRIPTION:Karol Dykiert zaprezentuje artykuł naukowy: Kumar\, S.M.\, Narasimhan\, S. and Murty Bhallamudi\, S.\, (2010). Parameter estimation in water distribution networks. Water Resources Management\, 24\, pp. 1251-1272. DOI 10.1007/s11269-009-9495-1\n\n
DTEND;TZID=Europe/Warsaw:20260304T101500
DTSTAMP:20260419T052930Z
DTSTART;TZID=Europe/Warsaw:20260304T091500
GEO:51.1098702;17.0584436
LOCATION:11a plac Grunwaldzki 11a plac Grunwaldzki 50-377 Wrocław województwo dolnośląskie Polska
ORGANIZER:Network Science Lab
STATUS:CONFIRMED
SUMMARY:Seminarium NSL - Karol Dykiert, Parameter estimation in water distribution networks
UID:a5f9f7be-bf54-4f72-83d4-d0918eba8b1a
URL:https://events.ai.pwr.edu.pl/events/a5f9f7be-bf54-4f72-83d4-d0918eba8b1a
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