9,596 research outputs found
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Few-shot node classification is tasked to provide accurate predictions for
nodes from novel classes with only few representative labeled nodes. This
problem has drawn tremendous attention for its projection to prevailing
real-world applications, such as product categorization for newly added
commodity categories on an E-commerce platform with scarce records or diagnoses
for rare diseases on a patient similarity graph. To tackle such challenging
label scarcity issues in the non-Euclidean graph domain, meta-learning has
become a successful and predominant paradigm. More recently, inspired by the
development of graph self-supervised learning, transferring pretrained node
embeddings for few-shot node classification could be a promising alternative to
meta-learning but remains unexposed. In this work, we empirically demonstrate
the potential of an alternative framework, \textit{Transductive Linear
Probing}, that transfers pretrained node embeddings, which are learned from
graph contrastive learning methods. We further extend the setting of few-shot
node classification from standard fully supervised to a more realistic
self-supervised setting, where meta-learning methods cannot be easily deployed
due to the shortage of supervision from training classes. Surprisingly, even
without any ground-truth labels, transductive linear probing with
self-supervised graph contrastive pretraining can outperform the
state-of-the-art fully supervised meta-learning based methods under the same
protocol. We hope this work can shed new light on few-shot node classification
problems and foster future research on learning from scarcely labeled instances
on graphs.Comment: Accepted to the First Learning on Graph Conference (LoG 2022
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many
real-world applications. However, existing methods mainly focus on networks
with single-typed nodes/edges and cannot scale well to handle large networks.
Many real-world networks consist of billions of nodes and edges of multiple
types, and each node is associated with different attributes. In this paper, we
formalize the problem of embedding learning for the Attributed Multiplex
Heterogeneous Network and propose a unified framework to address this problem.
The framework supports both transductive and inductive learning. We also give
the theoretical analysis of the proposed framework, showing its connection with
previous works and proving its better expressiveness. We conduct systematical
evaluations for the proposed framework on four different genres of challenging
datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results
demonstrate that with the learned embeddings from the proposed framework, we
can achieve statistically significant improvements (e.g., 5.99-28.23% lift by
F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link
prediction. The framework has also been successfully deployed on the
recommendation system of a worldwide leading e-commerce company, Alibaba Group.
Results of the offline A/B tests on product recommendation further confirm the
effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn
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