Advances in Complex Systems Modelling via Hypergraphs II
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 1021
Special Issue Editors
Interests: soft computing; pattern recognition; computational intelligence; supervised and unsupervised data driven modeling techniques; neural networks; fuzzy systems; evolutionary algorithm
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; computational intelligence; big data analysis; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last few decades, network science has emerged as a breakthrough field via which researchers can study and investigate complex systems.
A graph (or network) is the archetype of (organized) complexity, where a set of nodes are connected to each other by mutual correlations (edges). Such topological and semantic data structures have been widely used to model telecommunication, computer, biological and social networks, just to name a few.
However, an intrinsic drawback of graphs is that they only account for pairwise relationships between nodes; indeed, by definition, an edge can only connect two nodes. This limits the modelling power offered by graphs to an extent, causing them to yield an incomplete description of the system under investigation.
Hypergraphs overcome these limitations by allowing hyperedges to connect simultaneously more than two nodes would. The greater modelling capabilities of multi-way relationships have been demonstrated in fields such as biology (e.g., protein–protein interaction networks) and social networks (e.g., collaboration networks). Yet, the power of hypergraphs is not limited to a mere representation of data. Hypergraphs and simplicial complexes also play a key role in the emergent field of topological data analysis, whose aim is to analyze a set of data (or point clouds) using techniques derived from topology and mathematics. In fact, rather than analyzing the data themselves (which can be difficult due to noise, high dimensionality, and so on), one can build a filtered set of simplicial complexes and study their properties.
This Special Issue aims to collect high-quality research papers within the research field of hypergraphs, embracing applications, theoretical conceptualizations and computational aspects. Papers that bridge the gap between hypergraphs and machine learning are particularly of interest. Position and survey papers are also welcome.
Dr. Antonello Rizzi
Dr. Alessio Martino
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- hypergraphs
- simplicial complexes
- topological data analysis
- machine learning
- pattern recognition
- manifold learning
- computational topology
- computational geometry
- persistent homology
- applied topology
- complex systems
