86 papers with code ·
Graphs

The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.

ASONAM 2019 • benedekrozemberczki/GEMSEC •

In this paper we propose GEMSEC - a graph embedding algorithm which learns a clustering of the nodes simultaneously with the embedding.

COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

KDD 2019 • benedekrozemberczki/EdMot

Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed.

SOTA for Community Detection on Cora

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training.

#4 best model for Node Classification on PPI

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION

Recently, Graph Neural Network (GNN) is proposed as a general and powerful framework to handle tasks on graph data, e. g., node embedding, link prediction and node classification.

GRAPH NEURAL NETWORK LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.

SOTA for Link Prediction on Cora

GRAPH CLASSIFICATION LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION SENTIMENT ANALYSIS TEXT CLASSIFICATION

Motivation: Graph embedding learning which aims to automatically learn low-dimensional node representations has drawn increasing attention in recent years.

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.

Chrisackerman1/Triple2Vec-Learning-Triple-Embeddings-from-Knowledge-Graphs

•We show that directly applying existing embedding techniques on the nodes of the line graph to learn edge embeddings is not enough in the context of knowledge graphs.

Moreover, node representations are regularized to reconstruct the graph structure.

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.