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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
ISHNE: Influence Self-attention for Heterogeneous Network Embedding
In recent years, Graph Neural Networks has received enormous attention from
academia for its huge potential of modeling the network traits such as
macrostructure and single node attributes. However, prior mainstream works
mainly focus on homogeneous network and lack the capacity to characterize the
network heterogeneous property. Besides, most previous literature cannot model
the influence under microscope vision, making it infeasible to model the joint
relation between the heterogeneity and mutual interaction within multiple
relation type. In this paper, we propose an Influence Self-attention network to
address the difficulties mentioned above. To model heterogeneity and mutual
interaction, we redesign attention mechanism with influence factor on the
single-type relation level, which learns the importance coefficient from its
adjacent neighbors under the same meta-path based patterns. To incorporate the
heterogeneous meta-path in a unified dimension, we developed a self-attention
based framework for meta-path relation fusion according to the learned
meta-path coefficient. Our experimental results demonstrate that our framework
not only achieve higher results than current state-of-the-art baselines, but
also show promising vision on depicting heterogeneous interactive relations
under complicated network structure
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