1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Persistence homologyof networks: methodsand applications Mehmet Emin AktasEsra AkbaşAhmed El Fatmaoui2019 год

Persistence homology of networks: methods and applications
статья из журнала

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: Information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, and biological networks. The primary challenge in this domain is measuring similarity or distance between networks based on topology. However, classical graph-theoretic measures are usually local and mainly based on differences between either node or edge measurements or correlations without considering the topology of networks such as the connected components or holes. In recent years, mathematical tools and deep learning based methods have become popular to extract the topological features of networks. Persistent homology (PH) is a mathematical tool in computational topology that measures the topological features of data that persist across multiple scales with applications ranging from biological networks to social networks. In this paper, we provide a conceptual review of key advancements in this area of using PH on complex network science. We give a brief mathematical background on PH, review different methods (i.e. filtrations) to define PH on networks and highlight different algorithms and applications where PH is used in solving network mining problems. In doing so, we develop a unified framework to describe these recent approaches and emphasize major conceptual distinctions. We conclude with directions for future work. We focus our review on recent approaches that get significant attention in the mathematics and data mining communities working on network data. We believe our summary of the analysis of PH on networks will provide important insights to researchers in applied network science.
Год издания: 2019
Авторы: Mehmet Emin Aktas, Esra Akbaş, Ahmed El Fatmaoui
Издательство: Springer Nature
Источник: Applied Network Science
Ключевые слова: Topological and Geometric Data Analysis, Bioinformatics and Genomic Networks, Tryptophan and brain disorders
Другие ссылки: Applied Network Science (PDF)
Applied Network Science (HTML)
DOAJ (DOAJ: Directory of Open Access Journals) (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)