70,840 research outputs found
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs
Heterogeneous graph neural networks (HGNNs) have been widely applied in
heterogeneous information network tasks, while most HGNNs suffer from poor
scalability or weak representation when they are applied to large-scale
heterogeneous graphs. To address these problems, we propose a novel
Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning
(RHCO) for large-scale heterogeneous graph representation learning. Unlike
traditional heterogeneous graph neural networks, we adopt the contrastive
learning mechanism to deal with the complex heterogeneity of large-scale
heterogeneous graphs. We first learn relation-aware node embeddings under the
network schema view. Then we propose a novel positive sample selection strategy
to choose meaningful positive samples. After learning node embeddings under the
positive sample graph view, we perform a cross-view contrastive learning to
obtain the final node representations. Moreover, we adopt the label smoothing
technique to boost the performance of RHCO. Extensive experiments on three
large-scale academic heterogeneous graph datasets show that RHCO achieves best
performance over the state-of-the-art models
Simple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed
rich structural and semantic information of a heterogeneous graph into node
representations. Existing HGNNs inherit many mechanisms from graph neural
networks (GNNs) over homogeneous graphs, especially the attention mechanism and
the multi-layer structure. These mechanisms bring excessive complexity, but
seldom work studies whether they are really effective on heterogeneous graphs.
This paper conducts an in-depth and detailed study of these mechanisms and
proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To
easily capture structural information, SeHGNN pre-computes the neighbor
aggregation using a light-weight mean aggregator, which reduces complexity by
removing overused neighbor attention and avoiding repeated neighbor aggregation
in every training epoch. To better utilize semantic information, SeHGNN adopts
the single-layer structure with long metapaths to extend the receptive field,
as well as a transformer-based semantic fusion module to fuse features from
different metapaths. As a result, SeHGNN exhibits the characteristics of simple
network structure, high prediction accuracy, and fast training speed. Extensive
experiments on five real-world heterogeneous graphs demonstrate the superiority
of SeHGNN over the state-of-the-arts on both accuracy and training speed.Comment: Accepted by AAAI 202
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs
Semantic relation prediction aims to mine the implicit relationships between
objects in heterogeneous graphs, which consist of different types of objects
and different types of links. In real-world scenarios, new semantic relations
constantly emerge and they typically appear with only a few labeled data. Since
a variety of semantic relations exist in multiple heterogeneous graphs, the
transferable knowledge can be mined from some existing semantic relations to
help predict the new semantic relations with few labeled data. This inspires a
novel problem of few-shot semantic relation prediction across heterogeneous
graphs. However, the existing methods cannot solve this problem because they
not only require a large number of labeled samples as input, but also focus on
a single graph with a fixed heterogeneity. Targeting this novel and challenging
problem, in this paper, we propose a Meta-learning based Graph neural network
for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the
graph structure between objects into multiple normalized subgraphs, then adopts
a two-view graph neural network to capture local heterogeneous information and
global structure information of these subgraphs. Secondly, MetaGS aggregates
the information of these subgraphs with a hyper-prototypical network, which can
learn from existing semantic relations and adapt to new semantic relations.
Thirdly, using the well-initialized two-view graph neural network and
hyper-prototypical network, MetaGS can effectively learn new semantic relations
from different graphs while overcoming the limitation of few labeled data.
Extensive experiments on three real-world datasets have demonstrated the
superior performance of MetaGS over the state-of-the-art methods
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
In this paper, we focus on graph representation learning of heterogeneous
information network (HIN), in which various types of vertices are connected by
various types of relations. Most of the existing methods conducted on HIN
revise homogeneous graph embedding models via meta-paths to learn
low-dimensional vector space of HIN. In this paper, we propose a novel
Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly
encode structural information of HIN without meta-path and achieve more
informative representations. With this method, domain experts will not be
needed to design meta-path schemes and the heterogeneous information can be
processed automatically by our proposed model. Specifically, we implicitly
represent heterogeneous information using the following two methods: 1) we
model the transformation between heterogeneous vertices through a projection in
low-dimensional entity spaces; 2) afterwards, we apply the graph neural network
to aggregate multi-relational information of projected neighborhood by means of
attention mechanism. We also present three extensions of HetSANN, i.e.,
voices-sharing product attention for the pairwise relationships in HIN,
cycle-consistency loss to retain the transformation between heterogeneous
entity spaces, and multi-task learning with full use of information. The
experiments conducted on three public datasets demonstrate that our proposed
models achieve significant and consistent improvements compared to
state-of-the-art solutions
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