Related papers: Meta-Path Learning for Multi-relational Graph Neural Networks

Meta-Path Learning for Multi-relational Graph Neural Networks

URL: http://arxiv.org/abs/2309.17113v2
Date: Mon, 20 Nov 2023 17:31:20 GMT
Title: Meta-Path Learning for Multi-relational Graph Neural Networks
Authors: Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger
Abstract summary: 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.
Score: 14.422104525197838
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths. However, the former approach faces challenges in the presence of many relations (e.g., knowledge graphs), while the latter requires substantial domain expertise to identify relevant meta-paths. In this work 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. Key element of our approach is a scoring function for measuring the potential informativeness of a relation in the incremental construction of the meta-path. Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations, and substantially outperforms existing multi-relational GNNs on synthetic and real-world experiments.

Related papers

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks [46.325577161493726]
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.
arXiv Detail & Related papers (2023-07-08T09:10:43Z)
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)
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation [61.114580368455236]
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. We propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors.
arXiv Detail & Related papers (2021-09-07T04:28:09Z)
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks [68.9026534589483]
RioGNN is a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation.
arXiv Detail & Related papers (2021-04-16T04:30:06Z)
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning [46.79356071007187]
We propose a holistic approach to jointly train the backbone network and the channel gating. We develop a federated meta-learning approach to jointly learn good meta-initializations for both backbone networks and gating modules.
arXiv Detail & Related papers (2020-11-25T04:26:23Z)
Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
arXiv Detail & Related papers (2020-08-19T04:53:31Z)
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)
Automated Relational Meta-learning [95.02216511235191]
We propose an automated relational meta-learning framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.
arXiv Detail & Related papers (2020-01-03T07:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.