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

Bonner, Stephen; Atapour-Abarghouei, Amir; Jackson, Philip T.; Brennan, John; Kureshi, Ibad; Theodoropoulos, Georgios; McGough, Andrew Stephen; Obara, Bogusław

doi:10.1109/bigdata47090.2019.9005545

Cited by 18 publications

(11 citation statements)

References 34 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%

“…A temporal neighbourhood aggregation (TNA) layer [96] stacks a GCN, a GRU and a linear layer. Bonner et al designs an encoder that stacks two TNA layers, to achieve a 2-hop convolution and employs variational sampling for use on link prediction.…”

Section: ) Integrated Dynamic Graph Neural Networkmentioning

confidence: 99%

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

2021

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“…Thus, GCN is combined with long short-term memory (LSTM) in CD-GCN ( Manessi, Rozza & Manzo, 2020 ), GC-LSTM ( Chen et al, 2018 ), GCRN ( Seo et al, 2016 ) or gated recurrent units (GRU) in T-GCN ( Zhao et al, 2019 ), DCRNN ( Li et al, 2018 ). Following these ideas, authors of TNA ( Bonner et al, 2019 ) and Res-RGNN ( Chen et al, 2019 ) added residual connections to propagate topological information between neighboring snapshots. Recently, some papers (GCN-GAN ( Lei et al, 2019 ), DynGAN ( Maheshwari et al, 2019 )) have proposed to use GANs in combination with RNNs.…”

Section: Related Workmentioning

confidence: 99%

Temporal network embedding framework with causal anonymous walks representations

Makarov

Savchenko²,

Korovko

et al. 2022

PeerJ Computer Science

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Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. We provide a benchmark pipeline for the evaluation of temporal network embeddings. This work provides the first comprehensive comparison framework for temporal network representation learning for graph machine learning problems involving node classification and link prediction in every available setting. The proposed model outperforms state-of-the-art baseline models. The work also justifies their difference based on evaluation in various transductive/inductive edge/node classification tasks. In addition, we show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks, involving credit scoring based on transaction data.

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“…Variational graph autoencoder (VGAE) [87] and Graph-GAN [88] employ these approaches to static graphs. These generative models are used in dynamic graphs to learn these data distributions over time [82,31,89,90,91,84,44].…”

Section: Approaches Based On Deep Learningmentioning

confidence: 99%

A Survey on Embedding Dynamic Graphs

Barros¹,

Mendonça²,

Vieira³

et al. 2021

Preprint

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Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization.However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output.We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.

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

Cited by 18 publications

References 34 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

Temporal network embedding framework with causal anonymous walks representations

A Survey on Embedding Dynamic Graphs

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