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As the popularity of graph data increases, there is a growing need to countthe occurrences of subgraph patterns of interest, for a variety ofapplications. Many graphs are massive in scale and also fully dynamic (withinsertions and deletions of edges), rendering exact computation of these countsto be infeasible. Common practice is, instead, to use a small set of edges as asample to estimate the counts. Existing sampling algorithms for fully dynamicgraphs sample the edges with uniform probability. In this paper, we show thatwe can do much better if we sample edges based on their individual properties.Specifically, we propose a weighted sampling algorithm called WSD forestimating the subgraph count in a fully dynamic graph stream, which samplesthe edges based on their weights that indicate their importance and reflecttheir properties. We determine the weights of edges in a data-driven fashion,using a novel method based on reinforcement learning. We conduct extensiveexperiments to verify that our technique can produce estimates with smallererrors while often running faster compared with existing algorithms.

Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams