Understanding Coarsening for Embedding Large-Scale Graphs

Akyildiz, Taha Atahan; Aljundi, Amro Alabsi; Kaya, Kamer

doi:10.1109/bigdata50022.2020.9377898

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…For completeness, here we also briefly analyze the coarsening phase. For a detailed evaluation, we refer the reader to [16]. We first use the coarsening algorithm naively with no heuristics, then only with ordering, and lastly with both ordering and hub 2 -restriction and report the results in Table 4.…”

Section: Experiments On Coarsening Performancementioning

confidence: 99%

Boosting Graph Embedding on a Single GPU

Aljundi¹,

Akyildiz²,

Kaya³

2021

IEEE Trans. Parallel Distrib. Syst.

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Section: Experiments On Coarsening Performancementioning

confidence: 99%

Boosting Graph Embedding on a Single GPU

Aljundi¹,

Akyildiz²,

Kaya³

2021

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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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.

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Understanding Coarsening for Embedding Large-Scale Graphs

Cited by 2 publications

References 22 publications

Boosting Graph Embedding on a Single GPU

Boosting Graph Embedding on a Single GPU

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

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