E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Chen, Jinyin; Zhang, Jian; Xu, Xuanheng; Fu, Chenbo; Zhang, Dan; Zhang, Qingpeng; Xuan, Qi

doi:10.1109/tsmc.2019.2932913

Cited by 131 publications

(69 citation statements)

References 50 publications

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“…It requires a large decoder, with both dense and LSTM layers, to predict the next graph. The E-LSTM-D approach [38] is also extremely similar to this model. • D-GCN: [20], [21]: A dynamic GCN, similar to approaches proposed in [20] and [21].…”

Section: • Gae [13]: a Non-probabilistic Graph Convolutionalmentioning

confidence: 86%

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Bonner

Atapour-Abarghouei

Jackson

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently disregarded during the learning process, resulting in suboptimal performance on certain temporal infernce tasks. To combat this, we introduce Temporal Neighbourhood Aggregation (TNA), a novel vertex representation model architecture designed to capture both topological and temporal information to directly predict future graph states. Our model exploits hierarchical recurrence at different depths within the graph to enable exploration of changes in temporal neighbourhoods, whilst requiring no additional features or labels to be present. The final vertex representations are created using variational sampling and are optimised to directly predict the next graph in the sequence. Our claims are reinforced by extensive experimental evaluation on both real and synthetic benchmark datasets, where our approach demonstrates superior performance compared to competing methods, out-performing them at predicting new temporal edges by as much as 23% on real-world datasets, whilst also requiring fewer overall model parameters.

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Section: • Gae [13]: a Non-probabilistic Graph Convolutionalmentioning

confidence: 86%

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Bonner

Atapour-Abarghouei

Jackson

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

show abstract

“…Then, they constructed a prediction model based on a convolutional neural network (CNN) to extract the pattern variation features and realize the multi-node link prediction. Chen et al [24] divided the dynamic network into a series of time-series snapshots and used an encoder to encode and characterize each network snapshot, and the long short-term memory (LSTM) model was constructed to extract the features in the encoded snapshots. Then, the features extracted by LSTM were amplified to the original dimensions of the network by a decoder to predict the topology of the network in the future.…”

Section: Machine Learning-based Prediction Methodsmentioning

confidence: 99%

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

2020

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Link prediction is to estimate the possibility of future links among nodes by utilizing known information such as network topology and node attributes. According to the characteristics of opportunistic networks (topological time-variation, node mobility and intermittent connections), this paper proposes a novel link prediction approach (IRWR-DBN) for opportunistic networks that is based on random walk and a deep belief network. First, we reconstruct the Markov probability transition matrix and define a similarity index-improved random walk with restart (IRWR). Second, we divide the opportunistic network into network snapshots. Then, the similarity matrix of each snapshot is calculated by using the IRWR index to construct a sample set. Finally, a predictive model is constructed based on a deep belief network which extracts the time-domain characteristics in the process of dynamic evolution of the opportunistic network. The experimental results on the ITC and MIT Reality datasets show that compared with methods, such as the similarity-based index (CN, AA, Katz, RA, RWR), convolutional neural network, and recurrent neural network, the proposed method is more accurate and stable. INDEX TERMS Opportunistic network, link prediction, random walk with restart, deep belief network, similarity index.

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“…There are several use cases for network models. They may be used as reference models [2], [6] or as realistic models [45]- [47], and depending on their purpose there are several tasks the model can be used for. These include:…”

Section: E Dynamic Network Modelsmentioning

confidence: 99%

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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E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Cited by 131 publications

References 50 publications

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

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

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