Distinguishing subsampled power laws from other heavy-tailed distributions

Autor: Sormunen, Silja, Leskelä, Lasse, Saramäki, Jari

Publikováno v: Physical Review E 109, 054308 (2024)

Distinguishing power-law distributions from other heavy-tailed distributions is challenging, and this task is often further complicated by subsampling effects. In this work, we evaluate the performance of two commonly used methods for detecting power

Externí odkaz: http://arxiv.org/abs/2404.09614

Community Detection on Block Models with Geometric Kernels

Autor: Avrachenkov, Konstantin, Kumar, B. R. Vinay, Leskelä, Lasse

We consider the community recovery problem on a one-dimensional random geometric graph where every node has two independent labels: an observed location label and a hidden community label. A geometric kernel maps the locations of pairs of nodes to pr

Externí odkaz: http://arxiv.org/abs/2403.02802

Older adults’ preferences in the utilization of digital health and social services: a qualitative analysis of responses to open-ended questions

Autor: Elina Laukka, Sanna Lakoma, Marja Harjumaa, Suvi Hiltunen, Henna Härkönen, Miia Jansson, Riikka-Leena Leskelä, Susanna Martikainen, Paula Pennanen, Anastasiya Verho, Paulus Torkki

Publikováno v: BMC Health Services Research, Vol 24, Iss 1, Pp 1-12 (2024)

Abstract Background While digital health and social services offer promising solutions, they often overlook the perspectives and needs of older adults. This study aims to comprehensively investigate the preferences of older adults regarding the use a

Externí odkaz: https://doaj.org/article/8cafede271264470b2801ea103398b6a

A network community detection method with integration of data from multiple layers and node attributes

Autor: Reittu, Hannu, Leskelä, Lasse, Räty, Tomi

Publikováno v: Published version: Network Science 2023

Multilayer networks are in the focus of the current complex network study. In such networks multiple types of links may exist as well as many attributes for nodes. To fully use multilayer -- and other types of complex networks in applications, the me

Externí odkaz: http://arxiv.org/abs/2305.13012

Multilayer hypergraph clustering using the aggregate similarity matrix

Autor: Alaluusua, Kalle, Avrachenkov, Konstantin, Kumar, B. R. Vinay, Leskelä, Lasse

Publikováno v: In: Dewar, M., Pra{\l}at, P., Szufel, P., Th\'eberge, F., Wrzosek, M. (eds) Algorithms and Models for the Web Graph. WAW 2023. Lecture Notes in Computer Science, vol 13894. Springer, Cham. pp. 83-98

We consider the community recovery problem on a multilayer variant of the hypergraph stochastic block model (HSBM). Each layer is associated with an independent realization of a d-uniform HSBM on N vertices. Given the similarity matrix containing the

Externí odkaz: http://arxiv.org/abs/2301.11657