# Graph Similarity

37 papers with code • 1 benchmarks • 3 datasets

## Most implemented papers

# Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements.

# 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.

# SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs.

# GREED: A Neural Framework for Learning Graph Distance Functions

To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee.

# Learning 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.

# Scalable Spectral Clustering Using Random Binning Features

Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.

# Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications

The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence.

# Message Passing Graph Kernels

The first component is a kernel between vertices, while the second component is a kernel between graphs.

# Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed.