86 research outputs found
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding
different relationships and their interactions in a complex system, where each
layer in a multilayer network depicts the topological structure of a group of
nodes corresponding to a particular relationship. The interactions among
different layers imply how the interplay of different relations on the topology
of each layer. For a single-layer network, network embedding methods have been
proposed to project the nodes in a network into a continuous vector space with
a relatively small number of dimensions, where the space embeds the social
representations among nodes. These algorithms have been proved to have a better
performance on a variety of regular graph analysis tasks, such as link
prediction, or multi-label classification. In this paper, by extending a
standard graph mining into multilayer network, we have proposed three methods
("network aggregation," "results aggregation" and "layer co-analysis") to
project a multilayer network into a continuous vector space. From the
evaluation, we have proved that comparing with regular link prediction methods,
"layer co-analysis" achieved the best performance on most of the datasets,
while "network aggregation" and "results aggregation" also have better
performance than regular link prediction methods
On Data Imbalance in Molecular Property Prediction with Pre-training
Revealing and analyzing the various properties of materials is an essential
and critical issue in the development of materials, including batteries,
semiconductors, catalysts, and pharmaceuticals. Traditionally, these properties
have been determined through theoretical calculations and simulations. However,
it is not practical to perform such calculations on every single candidate
material. Recently, a combination method of the theoretical calculation and
machine learning has emerged, that involves training machine learning models on
a subset of theoretical calculation results to construct a surrogate model that
can be applied to the remaining materials. On the other hand, a technique
called pre-training is used to improve the accuracy of machine learning models.
Pre-training involves training the model on pretext task, which is different
from the target task, before training the model on the target task. This
process aims to extract the input data features, stabilizing the learning
process and improving its accuracy. However, in the case of molecular property
prediction, there is a strong imbalance in the distribution of input data and
features, which may lead to biased learning towards frequently occurring data
during pre-training. In this study, we propose an effective pre-training method
that addresses the imbalance in input data. We aim to improve the final
accuracy by modifying the loss function of the existing representative
pre-training method, node masking, to compensate the imbalance. We have
investigated and assessed the impact of our proposed imbalance compensation on
pre-training and the final prediction accuracy through experiments and
evaluations using benchmark of molecular property prediction models
- …