8 research outputs found
Beyond topological persistence: Starting from networks
Persistent homology enables fast and computable comparison of topological
objects. However, it is naturally limited to the analysis of topological
spaces. We extend the theory of persistence, by guaranteeing robustness and
computability to significant data types as simple graphs and quivers. We focus
on categorical persistence functions that allow us to study in full generality
strong kinds of connectedness such as clique communities, -vertex and
-edge connectedness directly on simple graphs and monic coherent categories.Comment: arXiv admin note: text overlap with arXiv:1707.0967
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
Graph classification aims to extract accurate information from
graph-structured data for classification and is becoming more and more
important in graph learning community. Although Graph Neural Networks (GNNs)
have been successfully applied to graph classification tasks, most of them
overlook the scarcity of labeled graph data in many applications. For example,
in bioinformatics, obtaining protein graph labels usually needs laborious
experiments. Recently, few-shot learning has been explored to alleviate this
problem with only given a few labeled graph samples of test classes. The shared
sub-structures between training classes and test classes are essential in
few-shot graph classification. Exiting methods assume that the test classes
belong to the same set of super-classes clustered from training classes.
However, according to our observations, the label spaces of training classes
and test classes usually do not overlap in real-world scenario. As a result,
the existing methods don't well capture the local structures of unseen test
classes. To overcome the limitation, in this paper, we propose a direct method
to capture the sub-structures with well initialized meta-learner within a few
adaptation steps. More specifically, (1) we propose a novel framework
consisting of a graph meta-learner, which uses GNNs based modules for fast
adaptation on graph data, and a step controller for the robustness and
generalization of meta-learner; (2) we provide quantitative analysis for the
framework and give a graph-dependent upper bound of the generalization error
based on our framework; (3) the extensive experiments on real-world datasets
demonstrate that our framework gets state-of-the-art results on several
few-shot graph classification tasks compared to baselines
The Automatic Detection of Dataset Names in Scientific Articles
We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub
A Review of Graph Neural Networks and Their Applications in Power Systems
Deep neural networks have revolutionized many machine learning tasks in power
systems, ranging from pattern recognition to signal processing. The data in
these tasks is typically represented in Euclidean domains. Nevertheless, there
is an increasing number of applications in power systems, where data are
collected from non-Euclidean domains and represented as graph-structured data
with high dimensional features and interdependency among nodes. The complexity
of graph-structured data has brought significant challenges to the existing
deep neural networks defined in Euclidean domains. Recently, many publications
generalizing deep neural networks for graph-structured data in power systems
have emerged. In this paper, a comprehensive overview of graph neural networks
(GNNs) in power systems is proposed. Specifically, several classical paradigms
of GNNs structures (e.g., graph convolutional networks) are summarized, and key
applications in power systems, such as fault scenario application, time series
prediction, power flow calculation, and data generation are reviewed in detail.
Furthermore, main issues and some research trends about the applications of
GNNs in power systems are discussed
Transfer Learning for Deep Learning on Graph-Structured Data
Graphs provide a powerful means for representing complex interactions between entities. Recently, new deep learning approaches have emerged for representing and modeling graph-structured data while the conventional deep learning methods, such as convolutional neural networks and recurrent neural networks, have mainly focused on the grid-structured inputs of image and audio. Leveraged by representation learning capabilities, deep learning-based techniques can detect structural characteristics of graphs, giving promising results for graph applications. In this paper, we attempt to advance deep learning for graph-structured data by incorporating another component: transfer learning. By transferring the intrinsic geometric information learned in the source domain, our approach can construct a model for a new but related task in the target domain without collecting new data and without training a new model from scratch. We thoroughly tested our approach with large-scale real-world text data and confirmed the effectiveness of the proposed transfer learning framework for deep learning on graphs. According to our experiments, transfer learning is most effective when the source and target domains bear a high level of structural similarity in their graph representations