# Enterprise Analytics using Graph Database and Graph-based Deep Learning

arXiv: https://arxiv.org/abs/2108.02867 · HTML: https://mlsanity.com/paper/2108.02867/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [An Attentive Survey of Attention Models](https://mlsanity.com/paper/1904.02874/) (2019)
- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)
- [Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis](https://mlsanity.com/paper/1909.10660/) (2019)
- [Efficient Representation Learning Using Random Walks for Dynamic Graphs](https://mlsanity.com/paper/1901.01346/) (2019)

## Built on by (most cited first, 2 of 2)

- [Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data](https://mlsanity.com/paper/2511.15112/) (2025)
- [Customer Relationship Intelligence: Integrating CRM and MDM for Enhanced Customer Engagement](https://mlsanity.com/paper/2609.06189/) (2026)

## Abstract (arXiv)

In a business-to-business (B2B) customer relationship management (CRM) use case, each client is a potential business organization/company with a solid business strategy and focused and rational decisions. This paper introduces a graph-based analytics approach to improve CRM within a B2B environment. In our approach, in the first instance, we have designed a graph database using the Neo4j platform. Secondly, the graph database has been investigated by using data mining and exploratory analysis coupled with cypher graph query language. Specifically, we have applied the graph convolution network (GCN) to enable CRM analytics to forecast sales. This is the first step towards a GCN-based binary classification based on graph databases in the domain of B2B CRM. We evaluate the performance of the proposed GCN model on graph databases and compare it with Random Forest (RF), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN). The proposed GCN approach is further augmented with the shortest path and eigenvector centrality attribute to significantly improve the accuracy of sales prediction. Experimental results reveal that the proposed graph-based deep learning approach outperforms the Random Forests (RsF) and two deep learning models, i.e., CNN and ANN under different combinations of graph features.