Investment return realizations often provide only partial distributional information, yet traditional portfolio optimization frameworks assume preserved statistical properties which can lead to modeling risk. To address this, we employ a robust optimization framework called Wasserstein distributionally robust optimization (WDRO) on the mean absolute deviation (MAD) of portfolio returns. This approach provides cross-distributional robustness via worst-case risk minimization over all distributions within a Wasserstein ball of radius ϵ centered on some empirical distribution estimate. However, as the number of assets increases, the optimization problem becomes high-dimensional and sensitive to signal noise. To alleviate this burden, we discard redundant assets from the investment universe by virtue of correlation market graph sparsification. To the best of our knowledge, the combination of market graph sparsification with the WDRO framework is a novel contribution introduced in this study. We demonstrate that this methodology delivers superior results in both computational efficiency and test-set return statistics when applied to real-world S&P500 stock price data from 2018 to 2024.