2,405 research outputs found
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Graphs represent interconnected structures prevalent in a myriad of
real-world scenarios. Effective graph analytics, such as graph learning
methods, enables users to gain profound insights from graph data, underpinning
various tasks including node classification and link prediction. However, these
methods often suffer from data imbalance, a common issue in graph data where
certain segments possess abundant data while others are scarce, thereby leading
to biased learning outcomes. This necessitates the emerging field of imbalanced
learning on graphs, which aims to correct these data distribution skews for
more accurate and representative learning outcomes. In this survey, we embark
on a comprehensive review of the literature on imbalanced learning on graphs.
We begin by providing a definitive understanding of the concept and related
terminologies, establishing a strong foundational understanding for readers.
Following this, we propose two comprehensive taxonomies: (1) the problem
taxonomy, which describes the forms of imbalance we consider, the associated
tasks, and potential solutions; (2) the technique taxonomy, which details key
strategies for addressing these imbalances, and aids readers in their method
selection process. Finally, we suggest prospective future directions for both
problems and techniques within the sphere of imbalanced learning on graphs,
fostering further innovation in this critical area.Comment: The collection of awesome literature on imbalanced learning on
graphs: https://github.com/Xtra-Computing/Awesome-Literature-ILoG
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
Fast Adaptation of Neural Networks
The ability to learn quickly from a few samples is a vital element of intelligence. Humans can reuse past knowledge and learn incredibly quickly. Also humans are able to interact with others to effectively guide their learning process. Computer vision systems for recognizing objects automatically from pixels are becoming commonplace in production systems. These modern computer vision systems use deep neural networks to automatically learn and recognize objects from data. Oftentimes, these deep neural networks used in production require a lot of data, take a long time to learn and forget old things when learning something new.
We build upon previous methods called Prototypical Networks and Model-Agnostic Meta-Learning (MAML) that enables machines to learn to recognize new objects with very little supervision from the user. We extend these methods to the semi-supervised few-shot learning scenario, where the few labeled samples are accompanied with (potentially many) unlabeled samples. Our proposed methods are able to learn better by also making use of the additional unlabeled samples. We note that in many real-world applications the adaptation performance can be significantly improved by requesting the few labels through user feedback (active adaptation). Further, our proposed methods can also adapt to new tasks without any labeled examples (unsupervised adaptation) when the new task has the same output space as the training tasks do
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