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Title: Deep Graph Matching Consensus

Paper content:
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes.
Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs.
We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process.
Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently.
We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-the-art.
Graph matching refers to the problem of establishing meaningful structural correspondences of nodes between two or more graphs by taking both node similarities and pairwise edge similarities into account (Wang et al., 2019b) .
Since graphs are natural representations for encoding relational data, the problem of graph matching lies at the heart of many real-world applications.
For example, comparing molecules in cheminformatics (Kriege et al., 2019b) , matching protein networks in bioinformatics (Sharan & Ideker, 2006; Singh et al., 2008) , linking user accounts in social network analysis (Zhang & Philip, 2015) , and tracking objects, matching 2D/3D shapes or recognizing actions in computer vision (Vento & Foggia, 2012) can be formulated as a graph matching problem.
The problem of graph matching has been heavily investigated in theory (Grohe et al., 2018) and practice (Conte et al., 2004) , usually by relating it to domain-agnostic distances such as the graph edit distance (Stauffer et al., 2017) and the maximum common subgraph problem (Bunke & Shearer, 1998) , or by formulating it as a quadratic assignment problem (Yan et al., 2016) .
Since all three approaches are NP-hard, solving them to optimality may not be tractable for large-scale, real-world instances.
Moreover, these purely combinatorial approaches do not adapt to the given data distribution and often do not consider continuous node embeddings which can provide crucial information about node semantics.
Recently, various neural architectures have been proposed to tackle the task of graph matching (Zanfir & Sminchisescu, 2018; Wang et al., 2019b; Zhang & Lee, 2019; Xu et al., 2019d; Derr et al., 2019; Heimann et al., 2018) or graph similarity (Bai et al., 2018; in a data-dependent fashion.
However, these approaches are either only capable of computing similarity scores between whole graphs (Bai et al., 2018; , rely on an inefficient global matching procedure (Zanfir & Sminchisescu, 2018; Wang et al., 2019b; Xu et al., 2019d; , or do not generalize to unseen graphs (Xu et al., 2019b; Derr et al., 2019; .
Moreover, they might be prone to match neighborhoods between graphs Typically, graph matching is formulated as an edge-preserving, quadratic assignment problem (Anstreicher, 2003; Gold & Rangarajan, 1996; Caetano et al., 2009; Cho et al., 2013) , i.e.,
subject to the one-to-one mapping constraints mentioned above.
This formulation is based on the intuition of finding correspondences based on neighborhood consensus (Rocco et al., 2018) , which shall prevent adjacent nodes in the source graph from being mapped to different regions in the target graph.
Formally, a neighborhood consensus is reached if for all node pairs (i,
j) ∈ V s × V t with S i,j = 1, it holds that for every node i ∈ N 1
(i) there exists a node j ∈ N 1
(j) such that S i ,j = 1.
In this work, we consider the problem of supervised and semi-supervised matching of graphs while employing the intuition of neighborhood consensus as an inductive bias into our model.
In the supervised setting, we are given pair-wise ground-truth correspondences for a set of graphs and want our model to generalize to unseen graph pairs.
In the semi-supervised setting, source and target graphs are fixed, and ground-truth correspondences are only given for a small subset of nodes.
However, we are allowed to make use of the complete graph structures.
We presented a two-stage neural architecture for learning node correspondences between graphs in a supervised or semi-supervised fashion.
Our approach is aimed towards reaching a neighborhood consensus between matchings, and can resolve violations of this criteria in an iterative fashion.
In addition, we proposed enhancements to let our algorithm scale to large input domains.
We evaluated our architecture on real-world datasets on which it consistently improved upon the state-of-the-art.

assistant: text: A two-stage GNN-based architecture to establish correspondences between two graphs that performs well on real-world tasks of image matching and knowledge graph entity alignment.