Graph Similarity
39 papers with code • 1 benchmarks • 3 datasets
IMDb Movie Reviews
IMDB-MULTI
Most implemented papers
Convolutional Set Matching for Graph Similarity
snap-stanford/neural-subgraph-learning-gnn
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We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs.
Inferring Networks From Random Walk-Based Node Similarities
In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i. e., commute times) or personalized PageRank scores.
Label Efficient Semi-Supervised Learning via Graph Filtering
liqimai/Efficient-SSL
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CVPR 2019
However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods.
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures
Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0. 814 and 0. 991 for the retrospective and the holdout analyses.
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
google-research/google-research
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Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.
Rethinking Kernel Methods for Node Representation Learning on Graphs
bluer555/KernelGCN
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NeurIPS 2019
Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification.
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
In this work, we provide a unifying framework that captures both of these operations, allowing one to simultaneously sparsify and coarsen a graph while preserving its large-scale structure.
Tree++: Truncated Tree Based Graph Kernels
At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.
Inferring Point Cloud Quality via Graph Similarity
We propose the GraphSIM -- an objective metric to accurately predict the subjective quality of point cloud with superimposed geometry and color impairments.
