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E-book Hypergraph Computation

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Penilaian

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The basic elements of many natural and artificial systems have dependencies on each other and call for correlation modeling and analytic methods to study these. The graphs are all around us from different perspective, and in general all the objects in the real world are defined based on their connections with other objects. These connections can be described as a graph, which is a common data structure in many cases. For example, graphs can depict the path in a city, where each path is represented with an edge to show the spatial connections between two locations. Graphs are also employed in the airline route map, in which each vertex is an airport and each edge is an airline. Recently, the most challenging data processing problem comes from the con-nected data, not just from the discrete ones. How to exploit the underneath connections behind the data has become an urgent and important task in many applications. Generally, graph has been used to formulate such correlations among data. A graph is a nonlinear data structure which is composed of a group of vertices and edges. Here, the vertices in a graph represent the subjects to be analyzed, and the edges in a graph are the lines connecting two vertices in the graph. Figure 1.1 shows an example of a graph. As a common way to model pairwise correlations among data, the components in a system can be represented by the vertices of a graph, and the associations between components are described by the edges. In this way, the association pattern is abstracted by the topological structure of the graph. In the past decades, it was not easy to apply graph theory in practice because of the limitation of computing power. In recent years, with the advancement of information technology and computing power, graph theory has demonstrated its practical values. As scales of data grow, scientists have come up with the concept of network science. The study of network science can be applied in various fields. For example, by studying the connection relationship between terminals on the Internet, the efficiency of data transmission in a network can be estimated. The study of interpersonal relationships can help understand the way people communicate with each other, disseminate information, and generate community. Studying the transmission chain of infectious diseases can help predict risks in time, thus interrupt transmission, and prevent their spread. People have also found that many biological, social, information, and other real networks have nontrivial structural patterns in the connections among their elements. These patterns reflect meaningful features of the whole network. For example, the small-world phenomenon (the average path length in the network does not increase significantly with the increase of the network size) widely exists in social networks [1]. Another example is scale-free network [2], in which the vertex degree distribution follows a power-law distribution, and this phenomenon is known in some biological metabolic networks .

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

006.3 DAI h

Penerbit

Singapore : Springer Nature., 2023

Deskripsi Fisik

251 hlm

Bahasa

English

ISBN/ISSN

9789819901852

Klasifikasi

006.3

Tipe Isi

text

Tipe Media

computer

Tipe Pembawa

online resource

Edisi

-

Subjek

Artificial Intelligence

Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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