Graph Similarity

21 papers with code • 1 benchmarks • 3 datasets

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Greatest papers with code

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

google-research/google-research 21 Apr 2019

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.

Feature Engineering Graph Attention +2

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.

Graph Similarity Metric Learning

Semantic Graph Based Place Recognition for 3D Point Clouds

kxhit/SG_PR 26 Aug 2020

First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud.

Graph Matching Graph Similarity

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.

General Classification Graph Similarity

clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

jocpae/clDice CVPR 2021

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.

Graph Similarity

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.

Graph Similarity

Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

yunshengb/UGraphEmb 1 Apr 2019

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.

General Classification Graph Classification +2

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.

Anomaly Detection Graph Clustering +2

Rethinking Kernel Methods for Node Representation Learning on Graphs

bluer555/KernelGCN NeurIPS 2019

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification.

General Classification Graph Classification +3