Graph Summarization with minimum bounded error

Many real-world phenomena can be represented as dynamic graphs, i.e., networks that change over time. The problem of dynamic graph summarization, i.e., to succinctly describe the evolution of a dynamic graph, has been widely studied. Existing methods typically use objective measures to find fixed structures such as cliques, stars, and cores. Most of the methods, however, do not consider the problem of online summarization, where the summary is incrementally conveyed to the analyst as the graph evolves, and (thus) do not take into account the knowledge of the analyst at a specific moment in time. As a specific instance of this generic problem, online summarization of dynamic graphs was introduced. We presented a framework to solve this problem, which has been built on the existing ideas related to maximum entropy principle, the minimum description length principle, and subjectively interesting subgraph patterns. We then introduced an efficient algorithm, called DSSG, which is followed by extensive experiments on real-world datasets. Through experimental results, we demonstrated the effectiveness of the proposed algorithm. The generated summaries are found to be informative with regard to the analyst’s prior knowledge about the data. We conclude this from the observed substantial compression ratios and the fact that compression equates learning. We have also found different sequences of patterns, which evolved over time in a network. We also presented a case study and demonstrated a potential use of the proposed method in the airline domain. Comparison of two different summaries of the airline network, using the scheduled and the actual flight data, revealed potentially informative events. As a part of future work, it would be interesting to extend the proposed method to incorporate a feature to capture periodicity of the patterns; another is to extend this method to multigraphs, weighted graphs, and attributed graphs. Finally, as a part of our ongoing/future work, we aim to develop a tool for interactive visualization and exploration of the found patterns.