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An Epidemiological Neural Network Exploiting Dynamic graph Structured Data Applied to the COVID-19 Outbreak

IEEE TRANSACTIONS ON BIG DATA 2021年第1期7卷 45-55页

作者： La Gatta, Valerio Moscato, Vincenzo Postiglione, Marco Sperli, Giancarlo Univ Naples Federico II Dept Elect & Informat Technol I-80125 Naples Italy

With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.

关键词： Data models Epidemics Predictive models Mathematical model COVID-19 Analytical models COVID-19 epidemic diffusion modeling data analytics data model spatio temporal data mining deep learning graph machine learning

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Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks

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ANNALS OF MEDICINE 2024年第1期56卷 2407063页

作者： Yu, Evan Du, Jingcheng Xiang, Yang Hu, Xinyue Feng, Jingna Luo, Xi Schneider, John A. Zhi, Degui Fujimoto, Kayo Tao, Cui Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX USA Mayo Clin Dept Artificial Intelligence & Informat Jacksonville FL 32224 USA Univ Texas Hlth Sci Ctr Houston Sch Publ Hlth Houston TX USA Univ Chicago Dept Med Chicago IL USA Univ chicago Dept Publ Hlth Sci Chicago IL USA

ObjectiveInvestigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different *** data from two cohorts of younger sexual minority men (SMM) from two U.S. cities (Chicago, IL, and Houston, TX) were collected between 2014 and 2016. Feature importance from graph attention network (GAT) models were determined using GNNExplainer. Domain adaptation was performed to examine model transferability from one city dataset to the other dataset, training with 100% of the source dataset with 30% of the target dataset and prediction on the remaining 70% from the target *** adaptation showed the ability of GAT to improve prediction over training with single city datasets. Feature importance analysis with GAT models in single city training indicated similar features across different cities, reinforcing potential application of GAT models in predicting HIV infections through domain *** models can be used to address the data sparsity issue in HIV study populations. They are powerful tools for predicting individual risk of HIV that can be further explored for better understanding of HIV transmission. In this study, we conducted domain adaptation between two urban areas to predict HIV status by incorporating social network *** employ GNNExplainer to elucidate the model's predictions on each city dataset, aligning them with knowledge of HIV risk *** adaptation resulted in better model performance over individual city training and has great potential for applications in modeling other sexually transmitted infections.

关键词： graph machine learning explainable artificial intelligence HIV domain adaptation

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graph clustering network with structure embedding enhanced

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PATTERN RECOGNITION 2023年第1期144卷

作者： Ding, Shifei Wu, Benyu Xu, Xiao Guo, Lili Ding, Ling China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Minist Educ Peoples Republ China Mine Digitizat Engn Res Ctr Xuzhou 221116 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Recently, deep clustering utilizing graph Neural Networks has shown good performance in the graph clustering. However, the structure information of graph was underused in existing deep clustering methods. Particularly, the lack of concern on mining different types structure information simultaneously. To tackle with the problem, this paper proposes a graph Clustering Network with Structure Embedding Enhanced (GC-SEE) which extracts nodes importance-based and attributes importance-based structure information via a feature attention fusion graph convolution module and a graph attention encoder module respectively. Additionally, it captures different orders-based structure information through multi-scale feature fusion. Finally, a self -supervised learning module has been designed to integrate different types structure information and guide the updates of the GC-SEE. The comprehensive experiments on benchmark datasets commonly used demonstrate the superiority of the GC-SEE. The results showcase the effectiveness of the GC-SEE in exploiting multiple types of structure for deep clustering.

关键词： graph machine learning graph Neural Network Deep clustering Self-supervised learning

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Knowledge graphs and their applications in drug discovery

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EXPERT OPINION ON DRUG DISCOVERY 2021年第9期16卷 1057-1069页

作者： MacLean, Finlay BenevolentAI Target Identificat London England

Introduction Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. Areas covered In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility;differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies. Expert opinion Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.

关键词： Biomedical knowledge graphs drug repurposing drug repositioning heterogeneous information networks graph machine learning network embeddings knowledge graph embedding network pharmacology network medicine

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Dynamic integration of unstructured data with BIM using a no-model approach based on machine learning and concept networks

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AUTOMATION IN CONSTRUCTION 2023年第1期150卷

作者： Sobhkhiz, Soroush El-Diraby, Tamer Univ Toronto Ctr Informat Syst Infrastructure & Construct Civil Engn Dept Toronto ON M5S 1A4 Canada

Accessing unstructured information through BIM-based platforms is essential for achieving integrated analytics especially, for facilities management where a wide range of unstructured data is required for effective decisionmaking. Previous research has explored linking textual data with BIM through the use of relational schemas, concept mapping, and ontologies. However, these methods are structured and static, failing to match the nonparametric and evolutionary nature of unstructured data. This study proposes an alternative approach where concept networks are used to represent the IFC data model. Using graph theory and natural language processing a classifier is trained for assigning text documents to their relevant IFC classes based on their conceptual network distances. Given that the classifier is trained on conceptual distances rather than the concepts themselves, it has the potential to be generalized to unseen classes with unseen concepts. Both the performance and the generalizability of the approach are evaluated in a case study.

关键词： Text to BIM Data integration Linked data Semantic networks graph machine learning Text classification

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Action Sequence Augmentation for Early graph-based Anomaly Detection 21

Action Sequence Augmentation for Early Graph-based Anomaly D...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhao, Tong Ni, Bo Yu, Wenhao Guo, Zhichun Shah, Neil Jiang, Meng Univ Notre Dame Notre Dame IN 46556 USA Snap Inc Santa Monica CA USA

ISBN: (纸本)9781450384469

The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have performed many such behaviors. Their performance is substantially hindered when the users' observed data is limited at an early stage, which needs to be improved to minimize financial loss. In this work, we propose Eland, a novel framework that uses action sequence augmentation for early anomaly detection. Eland utilizes a sequence predictor to predict next actions of every user and exploits the mutual enhancement between action sequence augmentation and user-action graph anomaly detection. Experiments on three real-world datasets show that Eland improves the performance of a variety of graph-based anomaly detection methods. With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.

关键词： graph machine learning Anomaly detection graph data augmentation

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Optimization of mixture models on time series networks encoded by visibility graphs: an analysis of the US electricity market

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COMPUTATIONAL MANAGEMENT SCIENCE 2023年第1期20卷 28-28页

作者： Mari, Carlo Baldassari, Cristiano Univ G dAnnunzio Dept Econ Viale Pindaro 42 I-65100 Pescara PE Italy Univ G dAnnunzio Dept Neurosci Imaging & Clin Sci Via Luigi Polacchi 11 I-66100 Chieti CH Italy

We propose a fully unsupervised network-based methodology for estimating Gaussian Mixture Models on financial time series by maximum likelihood using the Expectation-Maximization algorithm. Visibility graph-structured information of observed data is used to initialize the algorithm. The proposed methodology is applied to the US wholesale electricity market. We will demonstrate that encoding time series through Visibility graphs allows us to capture the behavior of the time series and the nonlinear interactions between observations well. The results reveal that the proposed methodology outperforms more established approaches.

关键词： Visibility graph Markov transition field graph embedding graph machine learning Topological data analysis

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JONNEE: Joint Network Nodes and Edges Embedding

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IEEE ACCESS 2021年 9卷 144646-144659页

作者： Makarov, Ilya Korovina, Ksenia Kiselev, Dmitrii HSE Univ Moscow 101000 Russia Univ Ljubljana Ljubljana 1000 Slovenia Artificial Intelligence Res Inst AIRI Moscow 105064 Russia

Recently, graph embedding models significantly improved the quality of graph machine learning tasks, such as node classification and link prediction. In this work, we propose a model called JONNEE (JOint Network Nodes and Edges Embedding), which learns node and edge embeddings under self-supervision via joint constraints in a given graph and its edge-to-vertex dual representation as a Line graph. The model uses two graph autoencoders with additional structural feature engineering and several regularization techniques to train for an adjacency matrix reconstruction task in an unsupervised setting. Experimental results show that our model performs on par with state-of-the-art undirected attribute graph embedding models and requires less number of epochs to achieve the same quality due to Line graph self-supervision under a unified embedding framework.

关键词： Task analysis machine learning Context modeling Deep learning Predictive models Feature extraction Matrix decomposition graph machine learning graph neural networks line graph link prediction network embedding network representation learning node classification

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graph machine learning in the Era of Large Language Models (LLMs)

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Shijie Wang Jiani Huang Zhikai Chen Yu Song Wenzhuo Tang Haitao Mao Wenqi Fan Hui Liu Xiaorui Liu Dawei Yin Qing Li The Hong Kong Polytechnic University Hong Kong Michigan State University USA North Carolina State University USA Baidu Inc China

graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, graph Neural Networks (GNNs) have emerged as a cornerstone in graph machine learning (graph ML), facilitating the representation and processing of graphs. Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of applications such as computer vision and recommender systems. This remarkable success has also attracted interest in applying LLMs to the graph domain. Increasing efforts have been made to explore the potential of LLMs in advancing graph ML’s generalization, transferability, and few-shot learning ability. Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability. Given the rapid progress of this research direction, a systematic review summarizing the latest advancements for graph ML in the era of LLMs is necessary to provide an in-depth understanding to researchers and practitioners. Therefore, in this survey, we first review the recent developments in graph ML. We then explore how LLMs can be utilized to enhance the quality of graph features, alleviate the reliance on labeled data, and address challenges such as graph Heterophily and out-of-distribution (OOD) generalization. Afterward, we delve into how graphs can enhance LLMs, highlighting their abilities to enhance LLM pre-training and inference. Furthermore, we investigate various applications and discuss the potential future directions in this promising field.

关键词： graph machine learning Large Language Models (LLMs) Pre-training and Fine-tuning Prompting and Representation learning

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