CLUSTER is a node classification tasks generated with Stochastic Block Models, which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. CLUSTER aims at identifying community clusters in a semi-supervised setting.
Variants: CLUSTER
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Node Classification | GatedGCN+ | Unlocking the Potential of Classic … | 2025-02-13 |
| Node Classification | NeuralWalker | Learning Long Range Dependencies on … | 2024-06-05 |
| Node Classification | CKGCN | CKGConv: General Graph Convolution with … | 2024-04-21 |
| Node Classification | TIGT | Topology-Informed Graph Transformer | 2024-02-03 |
| Node Classification | EIGENFORMER | Graph Transformers without Positional Encodings | 2024-01-31 |
| Node Classification | GRIT | Graph Inductive Biases in Transformers … | 2023-05-27 |
| Node Classification | GPTrans-Nano | Graph Propagation Transformer for Graph … | 2023-05-19 |
| Node Classification | Exphormer | Exphormer: Sparse Transformers for Graphs | 2023-03-10 |
| Node Classification | ARGNP | Automatic Relation-aware Graph Network Proliferation | 2022-05-31 |
| Node Classification | GPS | Recipe for a General, Powerful, … | 2022-05-25 |
| Node Classification | EGT | Global Self-Attention as a Replacement … | 2021-08-07 |
| Node Classification | GatedGCN-PE | Benchmarking Graph Neural Networks | 2020-03-02 |
Recent papers with results on this dataset: