Temporal network embedding using graph attention network

Mohan, Anuraj; Pramod, K Varshney

doi:10.1007/s40747-021-00332-x

Cited by 10 publications

(7 citation statements)

References 50 publications

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“…Finally, the studies by Yang et al ( 2022a ) proposed a hyperbolic temporal graph network on the Poincaré ball model of hyperbolic space; the temporal network embedding using graph attention network by Mohan and Pramod ( 2021 ); the embedding based on a variational autoencoder able to capture the evolution of temporal networks by Jiao et al ( 2022 ), ConMNCI by Liu et al ( 2022 ) that inductively mines local and communal influences. The authors of ConMNCI suggested an aggregator function that combines local and global influences to produce node embeddings at any time and presented the concept of continuous learning to strengthen inductive learning; the continuous-time dynamic network embeddings by Nguyen et al ( 2018 ), the causal anonymous walk representations for temporal network embedding by Makarov et al ( 2022 ); and TempNodeEmb by Abbas et al ( 2023 ).…”

Section: Graph Embedding Algorithmsmentioning

confidence: 99%

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Lecca,

Lecca

2023

Front. Artif. Intell.

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Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.

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Section: Graph Embedding Algorithmsmentioning

confidence: 99%

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Lecca,

Lecca

2023

Front. Artif. Intell.

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“…Some spatial methods focus on improving model capacity by introducing an attention mechanism to the graph domain, such as the Graph attention network (GAT), which adopts a self-attention mechanism to learn the weighting function [4]. Developments of GAT, such as Dual-primal graph convolutional network (DPGCN) [22] generalized GAT by using convolutions on nodes and edges, giving a better performance, Temporal graph attention network (TempGAN) learns node representations from continuous-time temporal graphs [23], and Hyperbolic graph attention network learns robust node representations of graphs in hyperbolic spaces [24]. The graph sample and aggregate method (GraphSage) [18], a nodebased spatial method, learns node, rather than graph, embeddings so it is graph-scale free and can be applied to large or evolving graphs.…”

Section: Related Workmentioning

confidence: 99%

Star Topology Convolution for Graph Representation Learning

Wang¹,

Feng²,

Zheng³

et al. 2022

Preprint

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<p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and maintains a good weight sharing property. To test the method, STC was compared with state-of-the-art graph convolutional methods in a supervised learning setting on nine node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental results showed that STC achieved state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed state-of-the-art essential protein identification methods. Since the similarity between CNN and STC, some techniques of CNN can be applied in STC. We showed a study of introducing transfer learning in STC. The experimental results showed that transfer learning can be used to improve the performance of STC.<br></p>

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“…Wang et al [31] proposed a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Mohan and Pramod [32] proposed a temporal graph attention network (TempGAN), whose aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network.…”

Section: Graph Attention Network Recommendationmentioning

confidence: 99%

Session-Based Graph Attention POI Recommendation Network

Zhang

Zhu

Yue

2022

Wireless Communications and Mobile Computing

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Point-of-interest (POI) recommendation which aims at predicting the locations that users may be interested in has attracted wide attentions due to the development of Internet of Things and location-based services. Although collaborative filtering based methods and deep neural network have gain great success in POI recommendation, data sparsity and cold start problem still exist. To this end, this paper proposes session-based graph attention network (SGANet for short) for POI recommendation by making use of regional information. Specifically, we first extract users’ features from the regional history check-in data in session windows. Then, we use graph attention network to learn users’ preferences for both POI and regional POI, respectively. We learn the long-term and short-term preferences of users by fusing the user embedding and POI ancillary information through gate recurrent unit. Finally, we conduct experiments on two real world location-based social network datasets Foursquare and Gowalla to verify the effectiveness of the proposed recommendation model and the experiments results show that SGANet outperformed the compared baseline models in terms of recommendation accuracy, especially in sparse data and cold start scenario.

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Temporal network embedding using graph attention network

Cited by 10 publications

References 50 publications

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Star Topology Convolution for Graph Representation Learning

Session-Based Graph Attention POI Recommendation Network

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