TemporalNode2vec: Temporal Node Embedding in Temporal Networks

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

“…For the temporal embedding baseline methods, we employ dynamicTriad (DT) [28] and temporalNode2vec (TN2V) [11].…”

Section: B Baseline Methodsmentioning

confidence: 99%

“…In this work, we focus on temporal graph autoencoders, i.e. those which return sequences of embeddings [11], [12], [16], [22], [28]. These techniques enhance the performances on the inference tasks that are sensitive to the temporal dimension.…”

Section: Introductionmentioning

confidence: 99%

Temporalizing static graph autoencoders to handle temporal networks

Haddad

Bothorel

Lenca

et al. 2021

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors of TemporalNode2vec ( Haddad et al, 2020 ) and Dyn-VGAE ( Mahdavi, Khoshraftar & An, 2019 ) propose to learn structural information of each snapshot by separate models. Haddad et al (2020) suggest to compute individual sets of random walks for each snapshot in Node2Vec fashion and learn final node embeddings jointly, while in Mahdavi, Khoshraftar & An (2019) autoencoders for each snapshot were trained in a consistent way to preserve similarity between consequent graph updates.…”

Section: Related Workmentioning

confidence: 99%

Temporal network embedding framework with causal anonymous walks representations

Makarov

Savchenko²,

Korovko

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

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.

show abstract

“…Most of the described above methods concentrate on the static embeddings, so it works poorly in the temporal scenario. Haddad et al (2019) propose the adaptation of Node2vec model to the dynamic case. Authors also introduce the task-specific temporal embeddings.…”

Section: Temporal Networkmentioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

show abstract

TemporalNode2vec: Temporal Node Embedding in Temporal Networks

Cited by 14 publications

References 23 publications

Temporalizing static graph autoencoders to handle temporal networks

Temporalizing static graph autoencoders to handle temporal networks

Temporal network embedding framework with causal anonymous walks representations

Survey on graph embeddings and their applications to machine learning problems on graphs

Contact Info

Product

Resources

About