A gentle introduction to deep learning for graphs

Bacciu, Davide; Errica, Federico; Micheli, Alessio; Podda, Marco

doi:10.1016/j.neunet.2020.06.006

Cited by 222 publications

(157 citation statements)

References 68 publications

Supporting

Mentioning

136

Contrasting

Unclassified

Order By: Relevance

“…We propose two ideas for rumor classification: 1) Detecting rumors based on the propagation graph can be regarded as a graph classification task. The common practice to solve the problem is to represent the whole graph with a single embedding (Bacciu, Errica, & Micheli, 2019). At that point, a standard classifier can be applied to output a graph prediction.…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Rumor detection based on propagation graph neural network with attention mechanism

Chen

et al. 2020

Expert Systems with Applications

101

View full text Add to dashboard Cite

Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based on the ensemble learning with propagation graph neural network). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task.

show abstract

Section: Classificationmentioning

confidence: 99%

“…The global graph embedding is obtained by aggregating all nodes linearly. However, sophisticated aggregation strategy may improve the classification performance (Bacciu et al, 2019). In our work, we aggregate the node representations for graph embedding with attention mechanism.…”

Section: Classificationmentioning

confidence: 99%

Rumor detection based on propagation graph neural network with attention mechanism

Chen

et al. 2020

Expert Systems with Applications

101

View full text Add to dashboard Cite

show abstract

“…GNN is becoming increasingly popular, which enjoys the major advantage of incorporating a sparse and discrete dependency structure between data points. A graph allows the representation of a multitude of associations through link orientation and data points [192].…”

Section: Gnnmentioning

confidence: 99%

Machine Learning for Landslides Prevention: A Survey

Ma¹,

Mei²

2020

Preprint

View full text Add to dashboard Cite

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

show abstract

“…These methods have better performance in prediction and ideal time complexity. However, these methods cannot capture the spatial dependencies and the evolution of the dependencies on the temporal domain simultaneously [7].…”

Section: Introductionmentioning

confidence: 99%

DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Gou

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy. INDEX TERMS Internet of Underwater Things (IoUT), deep learning, spatio-temporal prediction, multivariate convolutional LSTM (MVC-LSTM) neural network, thermocline.

show abstract

A gentle introduction to deep learning for graphs

Cited by 222 publications

References 68 publications

Rumor detection based on propagation graph neural network with attention mechanism

Rumor detection based on propagation graph neural network with attention mechanism

Machine Learning for Landslides Prevention: A Survey

DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Contact Info

Product

Resources

About