Dynamic Network Embeddings: From Random Walks to Temporal Random Walks

Nguyen, Giang; Lee, John Boaz; Rossi, Ryan A.; Ahmed, Nesreen K.; Koh, Eunyee; Kim, Sung‐Chul

doi:10.1109/bigdata.2018.8622109

Cited by 49 publications

(26 citation statements)

References 18 publications

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“…• CTDNE (Nguyen et al 2018): This is a dynamic embedding method that learns representations from temporal random walks that represent actual temporally valid sequences of node interactions.…”

Section: Baseline Methods and Experimental Settingsmentioning

confidence: 99%

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Temporal Network Embedding with High-Order Nonlinear Information

Qiu

et al. 2020

AAAI

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Temporal network embedding, which aims to learn the low-dimensional representations of nodes in temporal networks that can capture and preserve the network structure and evolution pattern, has attracted much attention from the scientific community. However, existing methods suffer from two main disadvantages: 1) they cannot preserve the node temporal proximity that capture important properties of the network structure; and 2) they cannot represent the nonlinear structure of temporal networks. In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. Specifically, we define three orders of temporal proximities by exploring network historical information with a time exponential decay model to quantify the temporal proximity between nodes. Then, we propose a novel deep guided auto-encoder to capture the highly nonlinear structure. Meanwhile, the training set of the guide auto-encoder is generated by the temporal random walk (TRW) algorithm. By training the proposed deep guided auto-encoder with a specific mini-batch stochastic gradient descent algorithm, HNIP can efficiently preserves the temporal proximities and highly nonlinear structure of temporal networks. Experimental results on four real-world networks demonstrate the effectiveness of the proposed method.

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Section: Baseline Methods and Experimental Settingsmentioning

confidence: 99%

“…DynamicTriad (Zhou et al 2018) imposes triad to model dynamic changes of network structure. CTDNE (Nguyen et al 2018) defines a valid walk sequence to capture the interaction sequence of temporal network. NetWalk (Yu et al 2018) learns representations based on deep neural network embedding and reservoir sampling.…”

Section: Related Workmentioning

confidence: 99%

Temporal Network Embedding with High-Order Nonlinear Information

Qiu

et al. 2020

AAAI

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show abstract

“…On the other hand, temporal network [54,55] whose edge connectivity varies over continuous time has been less studied from a network embedding perspective. One stateof-the-art work in this direction is continuous-time dynamic network embeddings (CTDNE) [19,56] which is a framework to adapt random walk and skip-gram-based approaches like deepwalk to temporal networks. CTDNE optimizes a skip-gram objective, so that the node that is closer in the temporal walk occupies closer when mapped to the vector space.…”

Section: Related Workmentioning

confidence: 99%

“…CTDNE optimizes a skip-gram objective, so that the node that is closer in the temporal walk occupies closer when mapped to the vector space. In the proposed work, the concept of temporal random walk [56,57] is used to capture the temporal information of the network and positive pointwise mutual information (PPMI) [58] to compute the temporal proximity between vertex pairs.…”

Section: Related Workmentioning

confidence: 99%

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Temporal network embedding using graph attention network

Mohan

Pramod

2021

Complex Intell. Syst.

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Graph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.

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Some Observations on Dynamic Random Walks and Network Renormalization

Chazelle

2019

Fundamentals of Computation Theory

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Dynamic Network Embeddings: From Random Walks to Temporal Random Walks

Cited by 49 publications

References 18 publications

Temporal Network Embedding with High-Order Nonlinear Information

Temporal Network Embedding with High-Order Nonlinear Information

Temporal network embedding using graph attention network

Some Observations on Dynamic Random Walks and Network Renormalization

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