Graph Similarity
39 papers with code • 1 benchmarks • 3 datasets
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Latest papers
S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it.
Do Similar Entities have Similar Embeddings?
nicolas-hbt/similar-embeddings
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A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i. e.}, position similar entities within the graph close to one another.
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
Towards Writer Retrieval for Historical Datasets
Our approach is evaluated on two historical datasets (Historical-WI and HisIR19).
Dynamic Vertex Replacement Grammars
daniel-gonzalez-cedre/dyverg
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Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data.
Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
oaimli/hgsum
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We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to represent different semantic units (e. g., words and sentences) of the documents.
Learning Feature Recovery Transformer for Occluded Person Re-identification
To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity.
Joint graph learning from Gaussian observations in the presence of hidden nodes
Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.
Privacy-Preserved Neural Graph Similarity Learning
rucaibox/ppgm
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To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning
mgwillia/unsupervised-analysis
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CVPR 2022
In this paper, we compare methods using performance-based benchmarks such as linear evaluation, nearest neighbor classification, and clustering for several different datasets, demonstrating the lack of a clear front-runner within the current state-of-the-art.
