Understanding graph embedding methods and their applications

Xu, Mengjia

doi:10.48550/arxiv.2012.08019

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…Consider the computation, 𝐴 = 𝑆𝑝𝑎𝑟𝑠𝑒 𝐵 ⊙ (𝐶𝐷) • 𝐸 that is used in graph embedding and graph neural networks [27,36]. The Hadamard product, or element-wise product, is denoted by ⊙ and matrix multiplication is denoted by •.…”

Section: Motivating Examplementioning

confidence: 99%

SparseLNR: Accelerating Sparse Tensor Computations Using Loop Nest Restructuring

Dias,

Sundararajah,

Saumya

et al. 2022

Preprint

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Sparse tensor algebra computations have become important in many real-world applications like machine learning, scientific simulations, and data mining. Hence, automated code generation and performance optimizations for tensor algebra kernels are paramount. Recent advancements such as the Tensor Algebra Compiler (TACO) greatly generalize and automate the code generation for tensor algebra expressions. However, the code generated by TACO for many important tensor computations remains suboptimal due to the absence of a scheduling directive to support transformations such as distribution/fusion.This paper extends TACO's scheduling space to support kernel distribution/loop fusion in order to reduce asymptotic time complexity and improve locality of complex tensor algebra computations. We develop an intermediate representation (IR) for tensor operations called branched iteration graph which specifies breakdown of the computation into smaller ones (kernel distribution) and then fuse (loop fusion) outermost dimensions of the loop nests, while the innermost dimensions are distributed, to increase data locality. We describe exchanges of intermediate results between space iteration spaces, transformation in the IR, and its programmatic invocation. Finally, we show that the transformation can be used to optimize sparse tensor kernels. Our results show that this new transformation significantly improves the performance of several real-world tensor algebra computations compared to TACO-generated code.

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Section: Motivating Examplementioning

confidence: 99%

SparseLNR: Accelerating Sparse Tensor Computations Using Loop Nest Restructuring

Dias,

Sundararajah,

Saumya

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Graph embedding Graph embedding converts high-dimensional sparse graphs into low-dimensional, dense and continuous vectors, maintaining the structural properties of a graph to the greatest extent [31]. [18] offer excellent performances in supervised learning tasks (e.g., graph classification), but acquiring large volume of labeled data is a challenge in itself.…”

Section: Figure 6: Computation Graph Of An Example Networkmentioning

confidence: 99%

DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks

Bai¹,

Ji²,

Li³

et al. 2022

Preprint

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Deep learning is attracting interest across a variety of domains, including natural language processing, speech recognition, and computer vision. However, model training is time-consuming and requires huge computational resources. Existing works on the performance prediction of deep neural networks, which mostly focus on the training time prediction of a few models, rely on analytical models and result in high relative errors.This paper investigates the computational resource demands of 29 classical deep neural networks and builds accurate models for predicting computational costs. We first analyze the profiling results of typical networks and demonstrate that the computational resource demands of models with different inputs and hyperparameters are not obvious and intuitive. We then propose a lightweight prediction approach DNNAbacus with a novel network structural matrix for network representation. DNNAbacus can accurately predict both memory and time cost for PyTorch and TensorFlow models, which is also generalized to different hardware architectures and can have zero-shot capability for unseen networks. Our experimental results show that the mean relative error (MRE) is 0.9% with respect to time and 2.8% with respect to memory for 29 classic models, which is much lower than the state-of-the-art works. CCS CONCEPTS• Computing methodologies → Learning settings; Parallel algorithms.

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SynDRep: A Knowledge Graph-Enhanced Tool based on Synergistic Partner Prediction for Drug Repurposing

Shalaby,

Rao,

Schultz

et al. 2024

Preprint

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Drug repurposing is gaining interest due to its high cost-effectiveness, low risks, and improved patient outcomes. However, most drug repurposing methods depend on drug-disease-target semantic connections of a single drug rather than insights from drug combination data. In this study, we propose SynDRep, a novel drug repurposing tool based on enriching knowledge graphs (KG) with drug combination effects. It predicts the synergistic drug partner with a commonly prescribed drug for the target disease, leveraging graph embedding and machine learning techniques. This partner drug is then repurposed as a single agent for this disease by exploring pathways between them in KG. Some of our selected candidates, such as miconazole and albendazole for Alzheimer's disease, have been validated, while others lack either a clear pathway or literature evidence for their use for the disease of interest. Therefore, complementing SynDRep with more specialized KG, and additional training data, would enhance its efficacy and offer cost-effective and timely solutions for patients.

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Understanding graph embedding methods and their applications

Cited by 3 publications

References 62 publications

SparseLNR: Accelerating Sparse Tensor Computations Using Loop Nest Restructuring

SparseLNR: Accelerating Sparse Tensor Computations Using Loop Nest Restructuring

DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks

SynDRep: A Knowledge Graph-Enhanced Tool based on Synergistic Partner Prediction for Drug Repurposing

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