Node Clustering
62 papers with code • 19 benchmarks • 14 datasets
Benchmarks
These leaderboards are used to track progress in Node Clustering
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Libraries
Use these libraries to find Node Clustering models and implementationsDatasets
IMDb Movie Reviews
Pubmed
Cora
Wiki Squirrel
Wiki-CS
Latest papers with no code
Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
To perform future forecasting of pressure and velocity on the challenging EAGLE dataset, we introduce a new mesh transformer.
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
To tackle this challenge, we propose a novel concept of meta-node for message passing that can learn enriched relational knowledge from complex heterogeneous graphs without any meta-paths and meta-graphs by explicitly modeling the relations among the same type of nodes.
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Contrastive learning has emerged as a powerful tool for graph representation learning.
Multi-Granularity Graph Pooling for Video-based Person Re-Identification
To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods.
Hub-aware Random Walk Graph Embedding Methods for Classification
In this paper, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem.
Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model
Accurate traffic conditions prediction provides a solid foundation for vehicle-environment coordination and traffic control tasks.
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assume that all input graphs share the same number of clusters.
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network
The final representations are the results of concatenating source and target embedding vectors.
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
