# Brand Network Booster: A new system for improving brand connectivity

arXiv: https://arxiv.org/abs/2309.16228 · HTML: https://mlsanity.com/paper/2309.16228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 76 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2210.05245/) (2022)
- [Distinctiveness Centrality in Social Networks](https://mlsanity.com/paper/1912.03391/) (2019)
- [The Semantic Brand Score](https://mlsanity.com/paper/2105.05781/) (2021)
- [Brand Intelligence Analytics](https://mlsanity.com/paper/2001.11479/) (2020)
- [Forecasting election results by studying brand importance in online news](https://mlsanity.com/paper/2105.05762/) (2021)
- [Forecasting financial markets with semantic network analysis in the COVID-19 crisis](https://mlsanity.com/paper/2009.04975/) (2020)
- [The power of reciprocal knowledge sharing relationships for startup success](https://mlsanity.com/paper/2105.11538/) (2021)
- [Improving the betweenness centrality of a node by adding links](https://mlsanity.com/paper/1702.05284/) (2017)
- [MetroSets: Visualizing Sets as Metro Maps](https://mlsanity.com/paper/2008.09367/) (2020)
- [As long as you talk about me: The importance of family firm brands and the contingent role of family-firm identity](https://mlsanity.com/paper/2110.13815/) (2021)
- [Forecasting consumer confidence through semantic network analysis of online news](https://mlsanity.com/paper/2105.04900/) (2021)
- [Editorial introduction: The power of words and networks](https://mlsanity.com/paper/2105.11263/) (2021)

## Abstract (arXiv)

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.