Continuous latent position models for instantaneous interactions
Bibliographic Data
| ID | 6161565 |
|---|---|
| Authors | Riccardo Rastelli (0000-0003-0982-2935, University College Dublin), Marco Corneli (0000-0002-9361-0080, Institut de Biologie Valrose) |
| Year | 2023 |
| Volume | 11 |
| Issue | 4 |
| Pages | 560-588 |
| Publication date | 2023-07-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2023.14 |
| OpenAlex | W3147664721 |
| Language | EN |
| Citations received | 1 |
| References cited | 37 |
We create a framework to analyze the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays and easily available. Examples of instantaneous interactions include email networks, phone call networks, and some common types of technological and transportation networks. Our framework relies on a novel extension of the latent position network model: we assume that the entities are embedded in a latent Euclidean space and that they move along individual trajectories which are continuous over time. These trajectories are used to characterize the timing and frequency of the pairwise interactions. We discuss an inferential framework where we estimate the individual trajectories from the observed interaction data and propose applications on artificial and real data
Data mining · Pairwise comparison · Phone · Position (finance · Complex Network Analysis Techniques · Computer Science · Data Management and Algorithms · Human Mobility and Location-Based Analysis · Artificial Intelligence
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |