Graph stores are becoming increasingly popular among NOSQL applications seeking flexibility and heterogeneity in managing linked data. Conceptually and in practice, applications ranging from social networks, knowledge representations to Internet of things benefit from graph data stores built on a combination of relational and non-relational technologies aimed at desired performance characteristics. The most common data access pattern in querying graph stores is to traverse from a node to its neighboring nodes. This paper studies the impact of such traversal pattern to common data caching policies in a partitioned data environment where a big graph is distributed across servers in a cluster. We propose and evaluate a new graph aware caching policy designed to keep and evict nodes, edges and their metadata optimized for query traversal pattern. The algorithm distinguishes the topology of the graph as well as the latency of access to the graph nodes and neighbors. We implemented graph aware caching on a distributed data store Apache HBase in the Hadoop family. Performance evaluations showed up to 15x speedup on the benchmark datasets preferring our new graph aware policy over non-aware policies. We also show how to improve the performance of existing caching algorithms for distributed graphs by exploiting the topology information.