Graph matching is a fruitful area in terms of both algorithms and theories. Given two graphs $$G_1 = (V_1, E_1)$$
G
1
=
(
V
1
,
E
1
)
and $$G_2 = (V_2, E_2)$$
G
2
=
(
V
2
,
E
2
)
, where $$V_1$$
V
1
and $$V_2$$
V
2
are the same or largely overlapped upon an unknown permutation $$\pi ^*$$
π
∗
, graph matching is to seek the correct mapping $$\pi ^*$$
π
∗
. In this paper, we exploit the degree information, which was previously used only in noiseless graphs and perfectly-overlapping Erdős–Rényi random graphs matching. We are concerned with graph matching of partially-overlapping graphs and stochastic block models, which are more useful in tackling real-life problems. We propose the edge exploited degree profile graph matching method and two refined variations. We conduct a thorough analysis of our proposed methods’ performances in a range of challenging scenarios, including coauthorship data set and a zebrafish neuron activity data set. Our methods are proved to be numerically superior than the state-of-the-art methods. The algorithms are implemented in the R (A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, 2020) package GMPro (GMPro: graph matching with degree profiles, 2020).
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