A Comparative Study of Some Point Process Models for Dynamic Networks
Modeling dynamic networks has attracted much interest in recent years, which helps understand networks’ behavior. Many works have been dedicated to modeling discrete-time networks, but less work is done for continuous-time networks. Point processes as powerful tools for modeling discrete events in c...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/1616116 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555205573476352 |
|---|---|
| author | S. Haleh S. Dizaji Saeid Pashazadeh Javad Musevi Niya |
| author_facet | S. Haleh S. Dizaji Saeid Pashazadeh Javad Musevi Niya |
| author_sort | S. Haleh S. Dizaji |
| collection | DOAJ |
| description | Modeling dynamic networks has attracted much interest in recent years, which helps understand networks’ behavior. Many works have been dedicated to modeling discrete-time networks, but less work is done for continuous-time networks. Point processes as powerful tools for modeling discrete events in continuous time have been widely used for modeling events over networks and their dynamics. These models have solid mathematical assumptions, making them interpretable but decreasing their generalizability for different datasets. Hence, neural point processes were introduced that don’t have strong assumptions on generative functions. However, these models can be impractical in the case of a large number of event types. This research presents a comparative study of different point process (Hawkes) models for continuous-time networks. Furthermore, a previously introduced neural point process (neural Hawkes) model is applied for modeling network interactions. In this work, network clustering is used for specifying interaction types. These methods are compared using different synthetic and real-world datasets, and their efficiency is evaluated on these datasets. The experiments represent that each model is appropriate for a group of datasets. In addition, the effect of clustering on results is discussed, and experiments for different clusters are presented. |
| format | Article |
| id | doaj-art-ce952051c1af4e32aa3655b176cc641d |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ce952051c1af4e32aa3655b176cc641d2025-02-03T05:49:19ZengWileyComplexity1099-05262022-01-01202210.1155/2022/1616116A Comparative Study of Some Point Process Models for Dynamic NetworksS. Haleh S. Dizaji0Saeid Pashazadeh1Javad Musevi Niya2Faculty of Electrical and Computer EngineeringFaculty of Electrical and Computer EngineeringFaculty of Electrical and Computer EngineeringModeling dynamic networks has attracted much interest in recent years, which helps understand networks’ behavior. Many works have been dedicated to modeling discrete-time networks, but less work is done for continuous-time networks. Point processes as powerful tools for modeling discrete events in continuous time have been widely used for modeling events over networks and their dynamics. These models have solid mathematical assumptions, making them interpretable but decreasing their generalizability for different datasets. Hence, neural point processes were introduced that don’t have strong assumptions on generative functions. However, these models can be impractical in the case of a large number of event types. This research presents a comparative study of different point process (Hawkes) models for continuous-time networks. Furthermore, a previously introduced neural point process (neural Hawkes) model is applied for modeling network interactions. In this work, network clustering is used for specifying interaction types. These methods are compared using different synthetic and real-world datasets, and their efficiency is evaluated on these datasets. The experiments represent that each model is appropriate for a group of datasets. In addition, the effect of clustering on results is discussed, and experiments for different clusters are presented.http://dx.doi.org/10.1155/2022/1616116 |
| spellingShingle | S. Haleh S. Dizaji Saeid Pashazadeh Javad Musevi Niya A Comparative Study of Some Point Process Models for Dynamic Networks Complexity |
| title | A Comparative Study of Some Point Process Models for Dynamic Networks |
| title_full | A Comparative Study of Some Point Process Models for Dynamic Networks |
| title_fullStr | A Comparative Study of Some Point Process Models for Dynamic Networks |
| title_full_unstemmed | A Comparative Study of Some Point Process Models for Dynamic Networks |
| title_short | A Comparative Study of Some Point Process Models for Dynamic Networks |
| title_sort | comparative study of some point process models for dynamic networks |
| url | http://dx.doi.org/10.1155/2022/1616116 |
| work_keys_str_mv | AT shalehsdizaji acomparativestudyofsomepointprocessmodelsfordynamicnetworks AT saeidpashazadeh acomparativestudyofsomepointprocessmodelsfordynamicnetworks AT javadmuseviniya acomparativestudyofsomepointprocessmodelsfordynamicnetworks AT shalehsdizaji comparativestudyofsomepointprocessmodelsfordynamicnetworks AT saeidpashazadeh comparativestudyofsomepointprocessmodelsfordynamicnetworks AT javadmuseviniya comparativestudyofsomepointprocessmodelsfordynamicnetworks |