Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2111.07840 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912699160461312 |
|---|---|
| author | Mantziou, Anastasia Keith, Sally Jacoby, David M. P. Lunagomez, Simon Mitra, Robin |
| author_facet | Mantziou, Anastasia Keith, Sally Jacoby, David M. P. Lunagomez, Simon Mitra, Robin |
| contents | Modelling multiple network data is crucial for addressing a wide range of applied research questions. However, there are many challenges, both theoretical and computational, to address. Network cycles are often of particular interest in many applications; for example in ecology a largely unexplored area has been how to incorporate network cycles within the inferential framework in an explicit way. The recently developed Spherical Network Family of models (SNF) offers a flexible formulation for modelling multiple network data that permits any type of metric. This has opened up the possibility to formulate network models that focus on network properties hitherto not possible or practical to consider. In this article we propose a novel network distance metric that measures similarities between networks with respect to their cycles, and incorporates this within the SNF model to allow inferences that explicitly capture information on cycles. These network motifs are of particular interest in ecological studies aimed at understanding competitive and hierarchical interactions. We further propose a novel computational framework to allow posterior inferences from the intractable SNF model for moderate-sized networks. Lastly, we apply the resulting methodology to a set of ecological network data studying aggressive interactions between species of fish. We show our model is able to make cogent inferences concerning the cycle behaviour amongst the species, and beyond those possible from a model that does not consider this network motif. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_07840 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Bayesian modelling and computation utilising directed cycles in multiple network data Mantziou, Anastasia Keith, Sally Jacoby, David M. P. Lunagomez, Simon Mitra, Robin Applications Modelling multiple network data is crucial for addressing a wide range of applied research questions. However, there are many challenges, both theoretical and computational, to address. Network cycles are often of particular interest in many applications; for example in ecology a largely unexplored area has been how to incorporate network cycles within the inferential framework in an explicit way. The recently developed Spherical Network Family of models (SNF) offers a flexible formulation for modelling multiple network data that permits any type of metric. This has opened up the possibility to formulate network models that focus on network properties hitherto not possible or practical to consider. In this article we propose a novel network distance metric that measures similarities between networks with respect to their cycles, and incorporates this within the SNF model to allow inferences that explicitly capture information on cycles. These network motifs are of particular interest in ecological studies aimed at understanding competitive and hierarchical interactions. We further propose a novel computational framework to allow posterior inferences from the intractable SNF model for moderate-sized networks. Lastly, we apply the resulting methodology to a set of ecological network data studying aggressive interactions between species of fish. We show our model is able to make cogent inferences concerning the cycle behaviour amongst the species, and beyond those possible from a model that does not consider this network motif. |
| title | Bayesian modelling and computation utilising directed cycles in multiple network data |
| topic | Applications |
| url | https://arxiv.org/abs/2111.07840 |