How Long to Read Dynamic Network Representation Based on Latent Factorization of Tensors

How Long Does it Take to Read Dynamic Network Representation Based on Latent Factorization of Tensors?

It takes the average reader 1 hour and 32 minutes to read Dynamic Network Representation Based on Latent Factorization of Tensors by Hao Wu

Assuming a reading speed of 250 words per minute. Learn more

Description

A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.

How long is Dynamic Network Representation Based on Latent Factorization of Tensors?

Dynamic Network Representation Based on Latent Factorization of Tensors by Hao Wu is 89 pages long, and a total of 23,051 words.

This makes it 30% the length of the average book. It also has 28% more words than the average book.

How Long Does it Take to Read Dynamic Network Representation Based on Latent Factorization of Tensors Aloud?

The average oral reading speed is 183 words per minute. This means it takes 2 hours and 5 minutes to read Dynamic Network Representation Based on Latent Factorization of Tensors aloud.

What Reading Level is Dynamic Network Representation Based on Latent Factorization of Tensors?

Dynamic Network Representation Based on Latent Factorization of Tensors is suitable for students ages 10 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

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