Graph Similarity
39 papers with code • 1 benchmarks • 3 datasets
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Latest papers
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
jindi-tju/CGMN
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As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.
GREED: A Neural Framework for Learning Graph Distance Functions
idea-iitd/greed
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To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning
Hence, this work proposes a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX).
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
canqin001/efficient_graph_similarity_computation
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NeurIPS 2021
For slow learning of graph similarity, this paper proposes a novel early-fusion approach by designing a co-attention-based feature fusion network on multilevel GNN features.
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
The success of subspace clustering depends on the assumption that the data can be separated into different subspaces.
Image-to-Image Retrieval by Learning Similarity between Scene Graphs
Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by graph neural networks.
Semantic Graph Based Place Recognition for 3D Point Clouds
kxhit/SG_PR
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First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud.
funcGNN: A Graph Neural Network Approach to Program Similarity
aravi11/funcGNN
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This study intends to examine the effectiveness of graph neural networks to estimate program similarity, by analysing the associated control flow graphs.
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
kleincup/MGMN
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In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
