This paper proposes a novel model for learning graph representations, called DNGR (Deep Neural Networks for Graph Representations). The model generates low-dimensional vector representations for each vertex by capturing graph structural information. Unlike previous methods that use sampling-based approaches, DNGR employs a random surfing model to directly capture graph structure, avoiding the need for sampling. It also introduces a stacked denoising autoencoder to extract complex features and model non-linearities, improving upon the linear dimension reduction techniques used in previous methods like SVD. The model is evaluated on clustering and visualization tasks using real-world datasets. Empirical results show that DNGR outperforms other state-of-the-art models in these tasks. The model's effectiveness is demonstrated through experiments on various datasets, including 20-NewsGroup, Wine, and Wikipedia. DNGR's ability to capture meaningful semantic, relational, and structural information is validated through tasks such as word similarity and clustering. Theoretical analysis shows that deep neural networks can capture non-linear information that conventional linear methods cannot. The random surfing model is argued to be more effective than traditional sampling methods for capturing graph structure. The use of stacked denoising autoencoders allows for better representation learning by capturing complex, non-linear relationships in the data. The model is also shown to be robust and scalable, with the ability to handle large-scale graph structures. The results demonstrate that DNGR provides better representations than alternative methods, particularly in capturing the complex relationships within graphs. The model's effectiveness is further supported by its performance on standard word similarity tasks, where it outperforms other methods like SVD and SGNS. Overall, DNGR offers a more effective approach to learning graph representations by leveraging deep learning techniques to capture non-linear relationships and complex structures.This paper proposes a novel model for learning graph representations, called DNGR (Deep Neural Networks for Graph Representations). The model generates low-dimensional vector representations for each vertex by capturing graph structural information. Unlike previous methods that use sampling-based approaches, DNGR employs a random surfing model to directly capture graph structure, avoiding the need for sampling. It also introduces a stacked denoising autoencoder to extract complex features and model non-linearities, improving upon the linear dimension reduction techniques used in previous methods like SVD. The model is evaluated on clustering and visualization tasks using real-world datasets. Empirical results show that DNGR outperforms other state-of-the-art models in these tasks. The model's effectiveness is demonstrated through experiments on various datasets, including 20-NewsGroup, Wine, and Wikipedia. DNGR's ability to capture meaningful semantic, relational, and structural information is validated through tasks such as word similarity and clustering. Theoretical analysis shows that deep neural networks can capture non-linear information that conventional linear methods cannot. The random surfing model is argued to be more effective than traditional sampling methods for capturing graph structure. The use of stacked denoising autoencoders allows for better representation learning by capturing complex, non-linear relationships in the data. The model is also shown to be robust and scalable, with the ability to handle large-scale graph structures. The results demonstrate that DNGR provides better representations than alternative methods, particularly in capturing the complex relationships within graphs. The model's effectiveness is further supported by its performance on standard word similarity tasks, where it outperforms other methods like SVD and SGNS. Overall, DNGR offers a more effective approach to learning graph representations by leveraging deep learning techniques to capture non-linear relationships and complex structures.