Estimation of Graph Features Based on Random Walks Using Neighbors' Properties
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917777612210176 |
|---|---|
| author | Hasegawa, Tsuyoshi Hironaka, Shiori Shudo, Kazuyuki |
| author_facet | Hasegawa, Tsuyoshi Hironaka, Shiori Shudo, Kazuyuki |
| contents | Using random walks for sampling has proven advantageous in assessing the characteristics of large and unknown social networks. Several algorithms based on random walks have been introduced in recent years. In the practical application of social network sampling, there is a recurrent reliance on an application programming interface (API) for obtaining adjacent nodes. However, owing to constraints related to query frequency and associated API expenses, it is preferable to minimize API calls during the feature estimation process. In this study, considering the acquisition of neighboring nodes as a cost factor, we introduce a feature estimation algorithm that outperforms existing algorithms in terms of accuracy. Through experiments that simulate sampling on known graphs, we demonstrate the superior accuracy of our proposed algorithm when compared to existing alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08599 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimation of Graph Features Based on Random Walks Using Neighbors' Properties Hasegawa, Tsuyoshi Hironaka, Shiori Shudo, Kazuyuki Social and Information Networks Using random walks for sampling has proven advantageous in assessing the characteristics of large and unknown social networks. Several algorithms based on random walks have been introduced in recent years. In the practical application of social network sampling, there is a recurrent reliance on an application programming interface (API) for obtaining adjacent nodes. However, owing to constraints related to query frequency and associated API expenses, it is preferable to minimize API calls during the feature estimation process. In this study, considering the acquisition of neighboring nodes as a cost factor, we introduce a feature estimation algorithm that outperforms existing algorithms in terms of accuracy. Through experiments that simulate sampling on known graphs, we demonstrate the superior accuracy of our proposed algorithm when compared to existing alternatives. |
| title | Estimation of Graph Features Based on Random Walks Using Neighbors' Properties |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2409.08599 |