Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
- URL: http://arxiv.org/abs/2307.03937v2
- Date: Sun, 4 Aug 2024 12:45:29 GMT
- Title: Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
- Authors: Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou Sun, Zhong Liu,
- Abstract summary: Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.
The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks.
- Score: 46.325577161493726
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm.
Related papers
- Meta-Path Learning for Multi-relational Graph Neural Networks [14.422104525197838]
We propose a novel approach to learn meta-paths and meta-path GNNs that are highly accurate based on a small number of informative meta-paths.
Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations.
arXiv Detail & Related papers (2023-09-29T10:12:30Z) - Meta-node: A Concise Approach to Effectively Learn Complex Relationships
in Heterogeneous Graphs [18.65171129524357]
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.
Unlike meta-paths and meta-graphs, meta-nodes do not require any pre-processing steps that require expert knowledge.
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.
arXiv Detail & Related papers (2022-10-26T05:04:29Z) - MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous
Information Networks [7.501059084460409]
Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types.
Existing meta-path generation approaches cannot fully exploit the rich textual information in HINs.
We propose MetaFill, a text-infilling-based approach for meta-path generation.
arXiv Detail & Related papers (2022-10-14T03:34:09Z) - Reinforced Meta-path Selection for Recommendation on Heterogeneous
Information Networks [18.35398976265591]
Heterogeneous Information Networks (HINs) capture complex relations among entities of various kinds.
Existing recommendation algorithms utilize hand-crafted meta-paths to extract semantic information from the networks.
We propose the Reinforcement learning-based Meta-path Selection framework to select effective meta-paths.
arXiv Detail & Related papers (2021-12-23T21:03:00Z) - SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations.
We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path.
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
arXiv Detail & Related papers (2021-12-12T14:18:18Z) - Contextualizing Meta-Learning via Learning to Decompose [125.76658595408607]
We propose Learning to Decompose Network (LeadNet) to contextualize the meta-learned support-to-target'' strategy.
LeadNet learns to automatically select the strategy associated with the right via incorporating the change of comparison across contexts with polysemous embeddings.
arXiv Detail & Related papers (2021-06-15T13:10:56Z) - mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for
Heterogeneous Information Network Embedding [15.400191040779376]
Heterogeneous information networks (HINs) are used to model objects with abundant information using explicit network structure.
Traditional network embedding algorithms are sub-optimal in capturing rich while potentially incompatible semantics provided by HINs.
mSHINE is designed to simultaneously learn multiple node representations for different meta-paths.
arXiv Detail & Related papers (2021-04-06T11:35:56Z) - Joint Semantics and Data-Driven Path Representation for Knowledge Graph
Inference [60.048447849653876]
We propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding.
Our proposed model is evaluated on two classes of tasks: link prediction and path query answering task.
arXiv Detail & Related papers (2020-10-06T10:24:45Z) - A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network
Representation Learning [52.83948119677194]
We propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning.
Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions.
We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba.
arXiv Detail & Related papers (2020-07-19T22:50:20Z) - GCN for HIN via Implicit Utilization of Attention and Meta-paths [104.24467864133942]
Heterogeneous information network (HIN) embedding aims to map the structure and semantic information in a HIN to distributed representations.
We propose a novel neural network method via implicitly utilizing attention and meta-paths.
We first use the multi-layer graph convolutional network (GCN) framework, which performs a discriminative aggregation at each layer.
We then give an effective relaxation and improvement via introducing a new propagation operation which can be separated from aggregation.
arXiv Detail & Related papers (2020-07-06T11:09:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.