The exponential growth of data, often referred to as big data, has transformed database architectures, leading to the emergence of various NoSQL models, including graph databases that effectively manage complex, highly connected data at scale. However, traditional historization methods face significant challenges in a graph context within big data warehouses, resulting in inefficiency during temporal analysis. In this paper, we propose the use of temporal subgraphs for data historization in big data graph warehouses. Temporal subgraphs also introduce a new theoretical abstraction, incorporating time into graph partitioning. This enhances both the efficiency and depth of graph-based OLAP analysis, making historical insights more accessible and scalable in large-scale graph data warehouses. Experimental evaluations demonstrate that our approach achieves improvement in query performance and significantly reduces storage redundancy compared to existing methods.