Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Mallick, Tanwi; Kiran, Mariam; Mohammed, Bashir; Balaprakash, Prasanna

doi:10.1109/bigdata50022.2020.9512748

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…[35,36] discuss how to utilize GNN in network calculus analysis. GNN is also helpful in link delay prediction [37] and network traffic prediction [38][39][40]. GNN has been introduced into automatic detection for Botnets, which is important to prevent DDoS attacks [41].…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

Ding¹,

Luo²,

Wang

et al. 2023

IEEE Trans. Netw. Sci. Eng.

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Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method -Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem by considering prior knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America, Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art rule-based and learning-based baselines. This work verifies the great potential of GNN for fine-grained IP geolocation.

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Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

Ding¹,

Luo²,

Wang

et al. 2023

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The pre-defined methods, which consist of distencemeature [3] [6] and similarity-meature [19] function, are commonly used to constructing the graph structure previously. To explore more complex topological informations, [12] and [8] employ an adaptive approach, while [9], [4] [20], [21] and [22] utilize diverse methods to generate dynamic graph at every time step. With the advance of attention mechanism, GAT, which regard the graph as a full-connected graph and use the weighted attention matrix to donate the correlation, is employed in [13] and [14].…”

Section: Related Work a Traffic Forecastingmentioning

confidence: 99%

Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

Tang¹,

Tang²,

Liu³

et al. 2022

Preprint

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Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of the smart cities and urban computing. Recently, Graph Neural Network truly outperforms the traditional methods. Nevertheless, the most conventional GNN based model works well while given a predefined graph structure. And the existing methods of defining the graph structures focus purely on spatial dependencies and ignored the temporal correlation. Besides, the semantics of the static pre-defined graph adjacency applyed during the whole training progress is always incomplete, thus overlooking the latent topologies that may fine-tune the model. To tackle these challenges, we proposed a new traffic forecasting framework-Spatio-Temporal Latent Graph Structure Learning networks (ST-LGSL). More specifically, the model employed a graph generator based on Multilayer perceptron and K-Nearest Neighbor, which learns the latent graph topological information from the entire data considering both spatial and temporal dynamics. Furthermore, with the initialization of MLP-kNN based on ground-truth adjacency matrix and similarity metric in kNN, ST-LGSL aggregates the topologies focusing on geography and node similarity. Additionnally, the generated graphs act as the input of spatio-temporal prediction module combined with the Diffusion Graph Convolutions and Gated Temporal Convolutions Networks. Experimental results on two benchmarking datasets in real world demonstrate that ST-LGSL outperforms various types of state-of-art baselines.

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