Tesseract: Mining Patterns in Large Dynamic Graphs

Graph pattern mining, i.e., finding instances of interesting patterns (matches) in a graph-structured dataset, has wide-ranging applications in social networks, chemistry, credit card fraud detection, and semantic web. Since graph mining applications can easily take hours to run, even on modest-sized graphs, it is desirable to incrementally maintain the set of all matches as the graph is updated instead of recomputing everything from scratch upon receiving updates. However, mining dynamic graphs introduces a new set of challenges. First, new algorithms are necessary to ensure correctness under updates and not miss any matches or produce duplicates. Second, a different software architecture is required to support high-throughput streams with thousands to millions of updates per second while maintaining mining results in real-time. In particular, this architecture must assign updates to workers in a balanced fashion while minimizing data transfer and synchronization across workers at scale.

We propose Tesseract, a distributed graph mining system for dynamic graphs. Tesseract introduces a novel change detection algorithm that efficiently determines the exact modifications for each update. Moreover, by favoring task parallelism over data parallelism, Tesseract can decompose a stream of graph updates into per-update mining tasks and dynamically assign these tasks to a set of distributed workers. Tesseract supports millions of updates per second with low latency and is several orders-of-magnitude faster than periodic recomputation on snapshots. Finally, Tesseract outperforms static mining systems on the entire graph, despite the overheads of supporting graph updates, thanks to its low memory requirements, efficient storage, and communication.

For more details, you can find the paper here.