  Here we study the problem of matched record clustering in unsupervised entity
resolution. We build upon a state-of-the-art probabilistic framework named the
Data Washing Machine (DWM). We introduce a graph-based hierarchical 2-step
record clustering method (GDWM) that first identifies large, connected
components or, as we call them, soft clusters in the matched record pairs using
a graph-based transitive closure algorithm utilized in the DWM. That is
followed by breaking down the discovered soft clusters into more precise entity
clusters in a hierarchical manner using an adapted graph-based modularity
optimization method. Our approach provides several advantages over the original
implementation of the DWM, mainly a significant speed-up, increased precision,
and overall increased F1 scores. We demonstrate the efficacy of our approach
using experiments on multiple synthetic datasets. Our results also provide
evidence of the utility of graph theory-based algorithms despite their sparsity
in the literature on unsupervised entity resolution.
