A parameter-free approach to lossless summarization of fully dynamic graphs

In large dynamic graphs, it is often impractical to store and process the entire graph. To contend with such graphs, lossless graph summarization is a compression technique that can provide a succinct graph representation without losing information. However, given a fully dynamic graph stream, the problem of lossless summarization is computationally and technically challenging. Although the state-of-the-art method performs well with respect to efficiency, its compression quality is usually unstable. This outcome occurs because it is a randomized algorithm and depends heavily on the pretuned parameters. In this paper, we propose a parameter-free approach to lossless summarization of a fully dynamic graph stream. In response to edge insertions and deletions, we first develop an incremental algorithm to maintain the summarization result by carefully exploring the influenced subgraph. Furthermore, we present a similarity-based algorithm to control the movement of vertices on the subgraph and thus guarantee the optimal result at each summarization update. To enhance the performance of our approach, we also propose two optimization techniques regarding candidate supernode refinement and destination supernode selection. The experimental results demonstrate that the proposed methods outperform the state-of-the-art methods by a large margin in terms of compression quality with comparable running time on the majority of datasets.