Convolutional Set Matching for Graph Similarity
We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs. As the core operation of graph similarity search, pairwise graph similarity computation is a challenging problem due to the NP-hard nature of computing many graph distance/similarity metrics. We demonstrate our model using the Graph Edit Distance (GED) as the example metric. Experiments on three real graph datasets demonstrate that our model achieves the state-of-the-art performance on graph similarity search.
Code (1)
Tasks
Graph Similarityset matchingSimilar Papers 제목 키워드 기반
Similarity Reasoning and Filtration for Image-Text Matching
Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words…
Cross-Modal RetrievalImage RetrievalImage-text matchingSentence+2Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity …
ClusteringCombinatorial OptimizationGraph ClassificationGraph Clustering+3HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning. However, previous GSL methods assume that graph…
Graph LearningGraph MatchingGraph SimilaritySEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning
Graph similarity computation (GSC) aims to quantify the similarity score between two graphs. Although recent GSC methods based on graph neural networks (GNNs) take advantage of intra-graph structures in message passing, …
Graph MatchingGraph SimilarityHierarchical Graph Matching Networks for Deep Graph Similarity Learning
While the celebrated graph neural networks yields effective representations for individual nodes of a graph, there has been relatively less success in extending to deep graph similarity learning. Recent work has conside…
Graph MatchingGraph Neural NetworkGraph Similarity