Graphs have become increasingly important in many applications and domains such as querying relationships in social networks or managing rich metadata generated in scientific computing. Many of these use cases require high-performance distributed graph databases for serving continuous updates from clients and, at the same time, answering complex queries regarding the current graph. These operations in graph databases, also referred to as online transaction processing (OLTP) operations, have specific design and implementation requirements for graph partitioning algorithms. In this research, we argue it is necessary to consider the connectivity and the vertex degree changes during graph partitioning. Based on this idea, we designed an Incremental Online Graph Partitioning (IOGP) algorithm that responds accordingly to the incremental changes of vertex degree. IOGP helps achieve better locality, generate balanced partitions, and increase the parallelism for accessing high-degree vertices of the graph. Over both real-world and synthetic graphs, IOGP demonstrates as much as 2x better query performance with a less than 10% overhead when compared against state-of-the-art graph partitioning algorithms.