Graph Similarity
39 papers with code • 1 benchmarks • 3 datasets
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Graph similarity learning for change-point detection in dynamic networks
The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history.
SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation
While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving graphs.
Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity
In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses.
Graph Similarity Description: How Are These Graphs Similar?
We formalize this problem as a model selection task using the Minimum Description Length principle, capturing the similarity of the input graphs in a common model and the differences between them in transformations to individual models.
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks.
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks
Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.
Graph-Graph Similarity Network
In this paper, we propose a Graph-Graph Similarity Network to tackle the graph classification problem by constructing a SuperGraph through learning the relationships among graphs.
Some Algorithms on Exact, Approximate and Error-Tolerant Graph Matching
We introduce a novel approach to measure graph similarity using geometric graphs.
A graph similarity for deep learning
The idea leads to a simple and efficient graph similarity, which we name Weisfeiler-Leman similarity (WLS).
