| Autor: |
Cancellieri, J., Didimo, W., Colladon, A. Fronzetti, Montecchiani, F., Vestrelli, R. |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Computers & Industrial Engineering 194, 110389 (2024) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1016/j.cie.2024.110389 |
| Popis: |
This paper presents a new decision support system offered for an in-depth analysis of semantic networks, which can provide insights for a better exploration of a brand's image and the improvement of its connectivity. In terms of network analysis, we show that this goal is achieved by solving an extended version of the Maximum Betweenness Improvement problem, which includes the possibility of considering adversarial nodes, constrained budgets, and weighted networks - where connectivity improvement can be obtained by adding links or increasing the weight of existing connections. Our contribution includes a new algorithmic framework and the integration of this framework into a software system called Brand Network Booster (BNB), which supports brand connectivity evaluation and improvement. We present this new system together with three case studies, and we also discuss its performance. Our tool and approach are valuable to both network scholars and in facilitating strategic decision-making processes for marketing and communication managers across various sectors, be it public or private. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
