Graph Similarity
39 papers with code • 1 benchmarks • 3 datasets
IMDb Movie Reviews
IMDB-MULTI
Latest papers with no code
Structure Your Data: Towards Semantic Graph Counterfactuals
With a focus on the visual domain, we represent images as scene graphs and obtain their GNN embeddings to bypass solving the NP-hard graph similarity problem for all input pairs, an integral part of the CE computation process.
MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation
Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks.
SALSA: Semantically-Aware Latent Space Autoencoder
We demonstrate by example that autoencoders may map structurally similar molecules to distant codes, resulting in an incoherent latent space that does not respect the structural similarities between molecules.
Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images
Tracking of plant cells in images obtained by microscope is a challenging problem due to biological phenomena such as large number of cells, non-uniform growth of different layers of the tightly packed plant cells and cell division.
Graph Edit Distance Learning via Different Attention
To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs.
Graph-Level Embedding for Time-Evolving Graphs
We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods.
Bures-Wasserstein Means of Graphs
Finding the mean of sampled data is a fundamental task in machine learning and statistics.
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference
Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS).
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
We also propose a novel NAS policy, called BOSHNAS, that leverages this new scheme, Bayesian modeling, and second-order optimization, to quickly train and use a neural surrogate model to converge to the optimal architecture.
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.
