kMatrix: A Space Efficient Streaming Graph Summarization Technique
This work addresses the challenge of memory efficiency in processing large-scale streaming graphs, offering an incremental improvement over prior summarization techniques.
The paper tackles the problem of summarizing massive streaming graphs for approximate property evaluation by introducing kMatrix, a technique that achieves significantly lower error for queries using the same memory space as existing methods like TCM and gMatrix.
The amount of collected information on data repositories has vastly increased with the advent of the internet. It has become increasingly complex to deal with these massive data streams due to their sheer volume and the throughput of incoming data. Many of these data streams are mapped into graphs, which helps discover some of their properties. However, due to the difficulty in processing massive streaming graphs, they are summarized such that their properties can be approximately evaluated using the summaries. gSketch, TCM, and gMatrix are some of the major streaming graph summarization techniques. Our primary contribution is devising kMatrix, which is much more memory efficient than existing streaming graph summarization techniques. We achieved this by partitioning the allocated memory using a sample of the original graph stream. Through the experiments, we show that kMatrix can achieve a significantly less error for the queries using the same space as that of TCM and gMatrix.