Graph analytics can be used to diversify a stock portfolio by analyzing the correlation between stocks. A graph model is created using Neo4j, where each stock ticker is represented as a node and the price and volume information for each stock ticker is stored as a linked list of stock trading days nodes. The Pearson similarity algorithm is used to calculate the correlation coefficient between most correlating stocks, and community detection algorithms are applied to identify clusters of correlating stocks. A simple linear regression model is then used to recommend top-performing stocks from each community. However, it's essential to note that this approach has limitations, such as only analyzing a 90-day window for NASDAQ-100 stocks and using a simplified correlation coefficient calculation. Further research and fine-tuning of the graph analysis may be necessary to improve the accuracy of the results.