Graph Similarity

27 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.

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.

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.

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.

Inferring Networks From Random Walk-Based Node Similarities

cnmusco/graph-similarity-learning NeurIPS 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.

Label Efficient Semi-Supervised Learning via Graph Filtering

liqimai/Efficient-SSL 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.