Query: active learning graph

Candidate papers:

abstract: 2D ferromagnetic (FM) semiconductors/half metals/metals are the key materials toward next generation spintronic devices. However, such materials are still rather rare and the material search space is too large to explore exhaustively. Here, an adaptive framework to accelerate the discovery of 2D intrinsic FM materials is developed, by combining advanced machine learning (ML) techniques with high throughput density functional theory calculations. Successfully, about 90 intrinsic FM materials with desirable bandgap and excellent thermodynamic stability are screened out and a database containing 1459 2D magnetic materials is set up. To improve the performance of ML models on small scale datasets like diverse 2D materials, a crystal graph multilayer descriptor using the elemental property is proposed, with which ML models achieve prediction accuracy over 90% on thermodynamic stability, magnetism, and bandgap. This study not only provides dozens of compelling FM candidates for future spintronics, but also paves a feasible route for ML based rapid screening of diverse structures and/or complex properties.
author_names: Shuaihua Lu

Qionghua Zhou

Yilv Guo

Yehui Zhang

Yilei Wu

Jinlan Wang
title: Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half Metals/Metals.
venue: Advanced materials
year: 2020

abstract: The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relationships can be modeled as a heterogeneous information network (HIN) In this paper, we attempt to solve the fake news detection problem with the support of a news oriented HIN. We propose a novel fake news detection framework, namely Adversarial Active Learning based Heterogeneous Graph Neural Network (AA HGNN) which employs a novel hierarchical attention mechanism to perform node representation learning in the HIN. AA HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data. An adversarial selector will be trained to query high value candidates for the active learning framework. When the adversarial active learning is completed, AA HGNN detects fake news by classifying news article nodes. Experiments with two real world fake news datasets show that our model can outperform text based models and other graph based models when using less labeled data benefiting from the adversarial active learning. As a model with generalizability, AA HGNN also has the ability to be widely used in other node classification related applications on heterogeneous graphs.
author_names: Yuxiang Ren

Bo Wang

Jiawei Zhang

Yi Chang
title: Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
venue: 2020 IEEE International Conference on Data Mining (ICDM)
year: 2020

abstract: Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is generally required to train these networks, which could be very expensive to obtain in some domains. In this paper, we study active learning for GNNs, i.e. how to efficiently label the nodes on a graph to reduce the annotation cost of training GNNs. We formulate the problem as a sequential decision process on graphs and train a GNN based policy network with reinforcement learning to learn the optimal query strategy. By jointly training on several source graphs with full labels, we learn a transferable active learning policy which can directly generalize to unlabeled target graphs. Experimental results on multiple datasets from different domains prove the effectiveness of the learned policy in promoting active learning performance in both settings of transferring between graphs in the same domain and across different domains.
author_names: Shengding Hu

Zheng Xiong

Meng Qu

Xingdi Yuan

Marc-Alexandre Cote

Zhiyuan Liu

Jian Tang
title: Graph Policy Network for Transferable Active Learning on Graphs
venue: NeurIPS
year: 2020

abstract: Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature focused approach that formulates both sequential and batch mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.
author_names: Hongjing Zhang

S S Ravi

Ian Davidson
title: A Graph Based Approach for Active Learning in Regression
venue: SDM
year: 2020

abstract: Author(s) Miller, Kevin; Li, Hao; Bertozzi, Andrea L Abstract: We present a novel adaptation of active learning to graph based semi supervised learning (SSL) under non Gaussian Bayesian models. We present an approximation of non Gaussian distributions to adapt previously Gaussian based acquisition functions to these more general cases. We develop an efficient rank one update for applying "look ahead" based methods as well as model retraining. We also introduce a novel "model change" acquisition function based on these approximations that further expands the available collection of active learning acquisition functions for such methods.
author_names: Kevin Miller

Hao Li

A Bertozzi
title: Efficient Graph Based Active Learning with Probit Likelihood via Gaussian Approximations
venue: ArXiv
year: 2020

abstract: Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can improve the achieved classification performance for a given budget on the number of queried labels. The best existing methods are based on graph neural networks, but they often perform poorly unless a sizeable validation set of labelled nodes is available in order to choose good hyperparameters. We propose a novel graph based active learning algorithm for the task of node classification in attributed graphs; our algorithm uses graph cognizant logistic regression, equivalent to a linearized graph convolutional neural network (GCN) for the prediction phase and maximizes the expected error reduction in the query phase. To reduce the delay experienced by a labeller interacting with the system, we derive a preemptive querying system that calculates a new query during the labelling process, and to address the setting where learning starts with almost no labelled data, we also develop a hybrid algorithm that performs adaptive model averaging of label propagation and linearized GCN inference. We conduct experiments on five public benchmark datasets, demonstrating a significant improvement over state of the art approaches and illustrate the practical value of the method by applying it to a private microwave link network dataset.
author_names: Florence Regol

Soumyasundar Pal

Yingxue Zhang

Mark J Coates
title: Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
venue: ICML
year: 2020

abstract: We present a graph based semi supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation. These constraints render our approach different from classical graph based SSL for vertex labels, which posits that tightly connected nodes share similar labels and leverages the graph structure accordingly to extrapolate from a few vertex labels to the unlabeled vertices. We derive bounds for our method's reconstruction error and demonstrate its strong performance on synthetic and real world flow networks from transportation, physical infrastructure, and the Web. Furthermore, we provide two active learning algorithms for selecting informative edges on which to measure flow, which has applications for optimal sensor deployment. The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence free. The second approach clusters the graph and selects bottleneck edges that cross cluster boundaries, which works well on flows with global trends.
author_names: J Jia

Michael T Schaub

Santiago Segarra

Austin R Benson
title: Graph based Semi Supervised Active Learning for Edge Flows
venue: KDD
year: 2019

abstract: Construct a knowledge graph is time consuming and the knowledge graph in the scientific domain requires extremely high labor costs due to it requires high prior knowledge to extract knowledge from resources. To build a scientific research knowledge graph, the most of input are papers, patent, the description of their project and some national program (such as National High Technology Research and Development Program of China, Major State Basic Research Development Program of China, General Program, Key Program and Major Program) which all of them are unstructured data, that make human participation are mostly necessary to measure the quality. In this paper, we design and proposed a framework using active learning; this framework can be used to extract entity and relation from unstructured science and technology research data. This framework combines the human and machine learning approach together, which is active learning, to help user extract entity from those unstructured data with less time cost. By using those data to construct a CKG as annotation label, it further implements active learning tools and helps the expert to rapidly annotate the data with high accuracy. Those knowledge graph constructed by this framework can be used to finding similar research area, finding similar researchers, finding popular research areas and so on.
author_names: Jing Dong

Peng Ren

Ye Wang

Jingjing Cao
title: A Framework Using Active Learning to Rapidly Perform Named Entity Extraction and Relation Recognition for Science and Technology Knowledge Graph
year: 2020

abstract: Creating a knowledge graph automatically from raw unstructured text has always been a job of domain expert which takes months to curate and refine. In this paper, we propose a domain independent semi automatic knowledge graph learning system that can be trained with less amount of data, to identify entities and relations from a large text corpus. The system performs the following tasks to extract knowledge graph from the text: (i) Named Entity Recognition (NER) and (ii) Relation Identification (Open Relation Extraction (OpenRE) and Classification) The system uses deep active learning to calculate confidence scores using maximum normalized log probability on each prediction for both NER, and relation identification. We experimented with both LSTM and transformer based models for NER and relation identification tasks.We achieved around 88% F1 score for the NER task on OntoNotes 5.0 English data set with 40% training data set and above 83% F1 score for relation identification on TACRED dataset. The OpenRE and relation classification systems were trained on domain specific datasets. To the best of our knowledge, we are the first to introduce a knowledge graph generation learning system with deep active learning.
author_names: Abhishek Pradhan

Ketan Kumar Todi

Anbarasan Selvarasu

Atish Sanyal
title: Knowledge Graph Generation with Deep Active Learning
venue: 2020 International Joint Conference on Neural Networks (IJCNN)
year: 2020

abstract: Predictive modeling of networked data finds many real world applications, such as fraud detection in social networks, drug discovery in biomedical networks, paper topic classification in citation networks, and so forth. Although the advanced machine learning approaches can help build reasonably accurate predictive models, their applicability is immensely hindered by the data labeling tasks, which are onerous, time consuming, and error prone. In this paper, we propose a novel active learning paradigm for networked data, named topology and content aware (TACA) active learning, aiming to minimize the number of labels while achieving a desirable level of model accuracy. Overall, TACA advances existing works from two aspects: (1) TACA makes no assumption on the network property, whereas most existing works only perform effectively on a locally consistent network in which linked nodes are expected to share the same labels and (2) TACA generates queries without relying on model performance, thereby enjoying robust predictive results even when noises exist in the queried labels. Both theoretical and empirical evidences are presented, substantiating the effectiveness of and optimism our approach.
author_names: Yi He

Xu Yuan

Nian-Feng Tzeng

Xindong Wu
title: Active Learning with Multi Granular Graph Auto Encoder
venue: 2020 IEEE International Conference on Data Mining (ICDM)
year: 2020