Temporal Link Prediction: A Survey

Divakaran, Aswathy; Mohan, Anuraj

doi:10.1007/s00354-019-00065-z

Cited by 82 publications

(48 citation statements)

References 91 publications

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“…We distinguish between two types of deep learning models: (i) Temporal restricted Boltzmann machines and (ii) Dynamic graph neural networks. Temporal restricted Boltzmann machines are probabilistic generative models which have been applied to the dynamic link prediction problem [4], [54]- [56]. Dynamic graph neural networks combine deep time series encoding with the aggregation of neighbouring nodes.…”

Section: E Dynamic Network Modelsmentioning

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: E Dynamic Network Modelsmentioning

confidence: 99%

“…For an introduction to SOAM see Snijders et al [53]. For surveys of representation learning on dynamic networks see Kazemi et al [11], Xie et al [12] and Barros et al [13], and for a survey of dynamic link prediction, including Temporal restricted Boltzmann machines, see Divakaran et al [54].…”

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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“…Some reviews have been published. They are pointing out the various approaches that exists towards temporal link prediction (Dhote et al 2013;Divakaran and Mohan 2020). Consequently, we will start with an exploration of four types of approaches presented therein.…”

Section: Related Workmentioning

confidence: 99%

“…Link prediction is often defined as the task to predict missing links based on the currently observable links in a network (Linyuan and Zhou 2011). Many realworld networks have temporal information on the times when the edges were created (Divakaran and Mohan 2020). Such temporal networks are also called dynamic or evolving networks.…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Supervised temporal link prediction in large-scale real-world networks

Bruin

Veenman

Herik

et al. 2021

Soc. Netw. Anal. Min.

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Link prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link prediction. This paper presents a systematic investigation of supervised temporal link prediction on 26 temporal, structurally diverse, real-world networks ranging from thousands to a million nodes and links. We analyse the relation between global structural properties of each network and the obtained temporal link prediction performance, employing a set of well-established topological features commonly used in the link prediction literature. We report on four contributions. First, using temporal information, an improvement of prediction performance is observed. Second, our experiments show that degree disassortative networks perform better in temporal link prediction than assortative networks. Third, we present a new approach to investigate the distinction between networks modelling discrete events and networks modelling persistent relations. Unlike earlier work, our approach utilises information on all past events in a systematic way, resulting in substantially higher link prediction performance. Fourth, we report on the influence of the temporal activity of the node or the edge on the link prediction performance, and show that the performance differs depending on the considered network type. In the studied information networks, temporal information on the node appears most important. The findings in this paper demonstrate how link prediction can effectively be improved in temporal networks, explicitly taking into account the type of connectivity modelled by the temporal edge. More generally, the findings contribute to a better understanding of the mechanisms behind the evolution of networks.

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“…Besides, statistical methods, such as Exponential Smoothing (EPS) [20] and Autoregressive Integrated Moving Average(ARIMA) [21], are also employed to predict temporal links with snapshots representation. However, Snapshots suffer from coarse-grained depiction of continuous changes, which probability result in poor predictive performance and misleading results [22]. Distinguished by the duration of interaction being negligible or not, Contact sequences and Interval graphs are proposed to illustrate the network dynamics.…”

Section: Related Workmentioning

confidence: 99%

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

2020

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Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

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Temporal Link Prediction: A Survey

Cited by 82 publications

References 91 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

Supervised temporal link prediction in large-scale real-world networks

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

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