Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
Existing message passing neural networks for heterogeneous graphs rely on the concepts of meta-paths or meta-graphs due to the intrinsic nature of heterogeneous graphs. However, the meta-paths and meta-graphs need to be pre-configured before learning and are highly dependent on expert knowledge to construct them. 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. Unlike meta-paths and meta-graphs, meta-nodes do not require any pre-processing steps that require expert knowledge. Going one step further, we propose a meta-node message passing scheme and apply our method to a contrastive learning model. In the experiments on node clustering and classification tasks, the proposed meta-node message passing method outperforms state-of-the-arts that depend on meta-paths. Our results demonstrate that effective heterogeneous graph learning is possible without the need for meta-paths that are frequently used in this field.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningGraph LearningNode ClusteringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networks
Meta-structures are widely used to define which subset of neighbors to aggregate information in heterogeneous information networks (HINs). In this work, we investigate existing meta-structures, including meta-path and me…
Node ClassificationEfficient block contrastive learning via parameter-free meta-node approximation
Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative samples which is unscalable and computa…
Contrastive LearningGPUGraph ClusteringSimultaneous Optimization of Neural Network Weights and Active Nodes using Metaheuristics
Optimization of neural network (NN) significantly influenced by the transfer function used in its active nodes. It has been observed that the homogeneity in the activation nodes does not provide the best solution. Theref…
Few-shot Node Classification with Extremely Weak Supervision
Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i.e., meta-training c…
ClassificationMeta-LearningNode ClassificationMulti-Granular Attention based Heterogeneous Hypergraph Neural Network
Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the p…
Graph Representation LearningNode ClassificationNode ClusteringRepresentation Learning