Advances in Complex Systems Modelling via Hypergraphs
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 10085
Special Issue Editors
Interests: machine learning; computational intelligence; big data analysis; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals
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
Special Issue Information
Dear Colleagues,
In the last few decades, network science has emerged as a breakthrough field in order to 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 and computer networks, biological networks and social networks, just to name a few.
However, an intrinsic drawback when dealing with graphs is that they only account for pairwise relationships between nodes; indeed, by definition, an edge can only connect two nodes. This somehow limits the modelling power offered by graphs, yielding an incomplete description of the system under investigation.
Hypergraphs overcome these limitations by allowing hyperedges to connect simultaneously more than two nodes. 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 the 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 itself (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 bridging the gap between hypergraphs and machine learning are particularly of interest. Position and survey papers are also welcome.
Dr. Alessio Martino
Prof. Dr. Antonello Rizzi
Guest Editors
Manuscript Submission Information
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Keywords
- Hypergraphs
- Simplicial Complexes
- Topological Data Analysis
- Machine Learning
- Pattern Recognition
- Manifold Learning
- Computational Topology
- Computational Geometry
- Persistent Homology
- Applied Topology
- Complex Systems
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