For distributed graph processing on massive graphs, a graph is partitioned into multiple
equally-sized parts which are distributed among machines in a compute cluster. In the last …

For computations on large-scale graphs, one often resorts to parallel algorithms. However,
parallel algorithms are difficult to write, debug and analyze. Worse still, it is difficult to make …

Real-world graphs are constantly evolving, which demands updates of the previous analysis
results to accommodate graph changes. By using the memoized previous computation state …

Modern graphs are not only large, but rapidly changing. The rate of change can vary
significantly along with the computational cost. Existing distributed graph analysis systems …

Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs,
social networks, and RDF graphs, keyword search for querying such graphs has been …

Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs,
social networks, and RDF graphs, keyword search for querying such graphs has been …

This dissertation presents my work in enhancing analytics on big graphs in aspects of both
efficiency and accuracy. Firstly, we propose a notion of partition transparency, such that …

The dynamic scaling of distributed computations plays an important role in the utilization of
elastic computational resources, such as the cloud. It enables the provisioning and de …