Place: Lecture hall T4, The Computer Science Building, Aalto University
Date: January 19, 2026
Format: 20 min talk + 10 min Q&A
Organizers: Tomomi Kito, Graduate School of Creative Science and Engineering, Waseda University; Petter Holme, Department of Computer Science, Aalto University.
Program
13:00–13:30
Eric Yanchenko, Akita International University
Hypothesis testing for community structure in temporal networks using e-values
Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community structure in temporally evolving networks is more scarce. In particular, there are currently no statistical methods available to test for the presence of community structure in a sequence of networks evolving over time. In this work, we propose a simple yet powerful test using e-values, an alternative to p-values that is more flexible in certain ways. Specifically, an e-value framework retains valid testing properties even after combining dependent information, a relevant feature in the context of testing temporal networks. We apply the proposed test to synthetic and real-world networks, demonstrating various features inherited from the e-value formulation and exposing some of the inherent difficulties of testing on temporal networks.
13:30–14:00
Carolina Mattsson, CENTAI
Modeling financial transactions via random walks on temporal networks
Financial transactions can be suitably generated via a dynamical process with many simultaneous random walks. We model M random walkers on an activity-driven temporal network of N nodes. Each random walker at an activated node follows the available temporal link with a certain probability, resulting in a transaction of some size. Despite its simplicity, this model gives rise to a rich phenomenology that reproduces key features of empirical data, including balance heterogeneity and high correlation between inflows and outflows. We provide an analytical representation and a path toward increasingly realistic models of financial transactions via integration with real payment records.
14:30–15:00
Manuel Cebrian, Spanish National Research Council
General scales unlock AI evaluation with explanatory and predictive power
Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activities. So far, benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems, given the low transferability across diverse tasks. In this paper, we introduce general scales for AI evaluation that can explain what common AI benchmarks really measure, extract ability profiles of AI systems, and predict their performance for new task instances, in- and out-of-distribution. Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate. Illustrated for 15 large language models and 63 tasks, high explanatory power is unleashed from inspecting the demand and ability profiles, bringing insights on the sensitivity and specificity exhibited by different benchmarks, and how knowledge, metacognition and reasoning are affected by model size, chain-of-thought and distillation. Surprisingly, high predictive power at the instance level becomes possible using these demand levels, providing superior estimates over black-box baseline predictors based on embeddings or finetuning, especially in out-of-distribution settings (new tasks and new benchmarks). The scales, rubrics, battery, techniques and results presented here represent a major step for AI evaluation, underpinning the reliable deployment of AI in the years ahead. (Collaborative platform: https://kinds-of-intelligence-cfi.github.io/ADELE/)
15:00–15:30
TBA, TBA University
Petter Holme 