A Parameter-Free Approach for Lossless Streaming Graph Summarization

In rapid and massive graph streams, it is often impractical to store and process the entire graph. Lossless graph summarization as a compression technique can provide a succinct graph representation without losing information. However, the problem of lossless streaming graph summarization is computationally and technically challenging. Although the state-of-the-art method performs well with respect to efficiency, its summarization quality is usually unstable and unsatisfactory. This is because it is a randomized algorithm and depends heavily on the pre-tuned parameters. In this paper, we propose a parameter-free lossless streaming graph summarization algorithm. As the graph changes over time, we incrementally maintain the summarization result, by carefully exploring the influenced subgraph, which is shown to be a bounded neighborhood of the inserted edge. To enhance the performance of our method, we further propose two optimization techniques regarding candidate supernodes refinement and destination supernode selection. The experiment results demonstrate that the proposed methods outperform the state-of-the-art by a large margin in terms of compression quality with comparable running time on the majority of datasets.