Graph Similarity

37 papers with code • 1 benchmarks • 3 datasets

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Most implemented papers

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

sk1712/gcn_metric_learning 7 Mar 2017

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

jocpae/clDice 16 Mar 2020

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

benedekrozemberczki/SimGNN WSDM '19 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining 2019

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

idea-iitd/greed 24 Dec 2021

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

Learning Networks from Random Walk-Based Node Similarities

cnmusco/graph-similarity-learning 23 Jan 2018

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

IBM/SpectralClustering_RandomBinning 25 May 2018

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

pinyuchen/FINGER 30 May 2018

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

Message Passing Graph Kernels

giannisnik/message_passing_graph_kernels 7 Aug 2018

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

yunshengb/GraphSim 10 Sep 2018

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.