Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams
This work addresses the need for efficient subgraph counting in large-scale, dynamic graphs, which is incremental as it improves upon existing sampling techniques by incorporating data-driven weighting.
The paper tackled the problem of accurately counting subgraph patterns in fully dynamic graph streams by proposing a weighted sampling algorithm (WSD) that uses reinforcement learning to assign edge weights, resulting in estimates with smaller errors and often faster runtime compared to existing uniform sampling methods.
As the popularity of graph data increases, there is a growing need to count the occurrences of subgraph patterns of interest, for a variety of applications. Many graphs are massive in scale and also fully dynamic (with insertions and deletions of edges), rendering exact computation of these counts to be infeasible. Common practice is, instead, to use a small set of edges as a sample to estimate the counts. Existing sampling algorithms for fully dynamic graphs sample the edges with uniform probability. In this paper, we show that we can do much better if we sample edges based on their individual properties. Specifically, we propose a weighted sampling algorithm called WSD for estimating the subgraph count in a fully dynamic graph stream, which samples the edges based on their weights that indicate their importance and reflect their properties. We determine the weights of edges in a data-driven fashion, using a novel method based on reinforcement learning. We conduct extensive experiments to verify that our technique can produce estimates with smaller errors while often running faster compared with existing algorithms.