Predicting Path Failure In Time-Evolving Graphs

Jia, Li; Han, Zhichao; Cheng, Hong; Su, Jiao; Wang, Pengyun; Zhang, Jianfeng; Pan, Lujia

doi:10.1145/3292500.3330847

Cited by 89 publications

(64 citation statements)

References 15 publications

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“…They may differ in which GNN and/or which RNN they use, the target use case or even the kind of graph they are built for, but the structures of the neural architecture are similar. Examples of these include GC-LSTM [20], LRGCN [23], RE-Net [95] and TNA [96].…”

Section: ) Integrated Dynamic Graph Neural Networkmentioning

confidence: 99%

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Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

2021

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Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology.INDEX TERMS Dynamic network models, graph neural networks, link prediction, temporal networks.

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Section: ) Integrated Dynamic Graph Neural Networkmentioning

confidence: 99%

“…Link prediction may for example be applied in knowledge graph completion [21], [22] or by recommender systems [18], [19]. DGNNs have also been used for novel tasks such as predicting path-failure in dynamic graphs [23], quantifying scientific impact [24], and detecting dominance, deception and nervousness [25].…”

Section: Introductionmentioning

confidence: 99%

Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

2021

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“…This is mainly due to the increased quantity of graph-structured data in real-world applications and the weak learning ability of convolutional neural networks (CNNs) when working with these data. GCNs and their variants have achieved promising performance with respect to Euclidean and non-Euclidean data, including but not limited to computer vision [1], [2], natural language processing [3], [4], publication citations [5], [6], social relationships [7], traffic prediction [12], [13], point clouds [8], [9], action recognition [10], [11], and recommender systems [14]- [16]. On the one hand, there is a large quantity of unlabeled graph data in nature, and labeling these data is very expensive.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Graph Convolutional Networks With Multi-Scale Neighborhood Pooling for Semi-Supervised Node Classification

et al. 2021

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Existing popular methods for semi-supervised node classification with high-order convolution improve the learning ability of graph convolutional networks (GCNs) by capturing the feature information from high-order neighborhoods. However, these methods with high-order convolution usually require many parameters and high computational complexity. To address these limitations, we propose HCNP, a new higher-order GCN for semi-supervised node learning tasks, which can simultaneously aggregate information of various neighborhoods by constructing high-order convolution. In HCNP, we reduce the number of parameters using a weight sharing mechanism and combine the neighborhood information via multi-scale neighborhood pooling. Further, HCNP does not require a large number of hidden units, and it fits a few parameters and exhibits low complexity. We show that HCNP matches GCNs in terms of complexity and parameters. Comprehensive evaluations on publication citation datasets (Citeseer, Pubmed, and Cora) demonstrate that the proposed methods outperform MixHop in most cases while maintaining lower complexity and fewer parameters and achieve state-of-the-art performance in terms of accuracy and parameters compared to other baselines.INDEX TERMS High-order convolution, graph convolutional networks, weight sharing, multi-scale neighborhood pooling, higher-order graph convolutional network, semi-supervised node classification.

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“…The different techniques of deep learning, such as transfer learning [18][19][20][21], multi-task learning [22][23][24], semisupervised [25] and unsupervised learning [26], enrich the application scenarios greatly. Many researchers explored the potential of neural network in the traffic predicting problems [27][28][29][30][31]. The data-driven deep learning methods can better model the nonlinearity of taxi demand data and the dynamic data trend, which can be categorized into two types based on model dependencies:…”

Section: Introductionmentioning

confidence: 99%

Dual temporal gated multi-graph convolution network for taxi demand prediction

Yang

Tang

Liu

2021

Neural Comput & Applic

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Taxi demand prediction is essential to build efficient traffic transportation systems for smart city. It helps to properly allocate vehicles, ease the traffic pressure and improve passengers’ experience. Traditional taxi demand prediction methods mostly rely on time-series forecasting techniques, which cannot model the nonlinearity embedded in data. Recent studies start to combine the Euclidean spatial features through grid-based methods. By considering the spatial correlations among different regions, we can capture how the temporal events have impacts on those with adjacent links or intersections and improve prediction precision. Some graph-based models are proposed to encode the non-Euclidean correlations as well. However, the temporal periodicity of data is often overlooked, and the study units are usually constructed as oversimplified grids. In this paper, we define places with specific semantic and humanistic experiences as study units, using a fuzzy set method based on adaptive kernel density estimation. Then, we introduce dual temporal gated multi-graph convolution network to predict the future taxi demand. Specifically, multi-graph convolution is used to model spatial correlations with graphs, including the neighborhood, functional similarities and landscape similarities based on street view images. As for the temporal dependencies modeling, we design the dual temporal gated branches to capture information hidden in both previous and periodic observations. Experiments on two real-world datasets show the effectiveness of our model over the baselines.

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Predicting Path Failure In Time-Evolving Graphs

Cited by 89 publications

References 15 publications

Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

Higher-Order Graph Convolutional Networks With Multi-Scale Neighborhood Pooling for Semi-Supervised Node Classification

Dual temporal gated multi-graph convolution network for taxi demand prediction

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