Pular para o conteúdo principal

livro Network Science - Albert-László Barabási

Disponível online em http://networksciencebook.com/

Capítulo 2

Exemplos de datasets de redes reais

 

The basic characteristics of ten networks used throughout this book to illustrate the tools of network science. The table lists the nature of their nodes and links, indicating if links are directed or undirected, the number of nodes (N) and links (L), and the average degree for each network. For directed networks the average degree shown is the average in- or out-degrees ‹k› = ‹kin›=‹kout› (see Equation (2.5)).  

In a complete graph each node is connected to every other node. A complete graph is often called a clique, a term frequently used in community identification. 
 
Sparseness has important consequences on the way we explore and store real networks. For example, when we store a large network in our computer, it is better to store only the list of links (i.e. elements for which Aij ≠ 0), rather than the full adjacency matrix, as an overwhelming fraction of the Aij elements are zero. Hence the matrix representation will block a huge chunk of memory, filled mainly with zeros
 
A bipartite graph (or bigraph) is a network whose nodes can be divided into two disjoint sets U and V such that each link connects a U-node to a V-node. Medicine offers another prominent example of a bipartite network: The Human Disease Network connects diseases to the genes whose mutations are known to cause or effect the corresponding disease. 

The shortest path between nodes i and j is the path with the fewest number of links. In practice we use the breadth first search (BFS) algorithm.Cycle: a path with the same start and end node. Eulerian Path: a path that traverses each link exactly once. Hamiltonian Path: a path that visits each node exactly once. 
 
The diameter of a network, denoted by dmax, is the maximum shortest path in the network.
A network is connected if all pairs of nodes in the network are connected.  
Clustering coefficient captures the degree to which the neighbors of a given node link to each other.
 
 
 

Comentários

  1. Livro para referência de conceitos em Teoria dos Grafos

    ResponderExcluir
  2. Várias explicações de como Ciência de Redes pode ser aplicado em várias áreas:
    Economic Impact: From Web Search to Social Networking
    Health: From Drug Design to Metabolic Engineering
    Security: Fighting Terrorism
    Epidemics: from Forecasting to Halting Deadly Viruses
    Neuroscience: Mapping the Brain
    Management: Uncovering the Internal Structure of an Organization

    Também explica "Six Deegree of Separation"

    ResponderExcluir

Postar um comentário

Sinta-se a vontade para comentar. Críticas construtivas são sempre bem vindas.

Postagens mais visitadas deste blog

Connected Papers: Uma abordagem alternativa para revisão da literatura

Durante um projeto de pesquisa podemos encontrar um artigo que nos identificamos em termos de problema de pesquisa e também de solução. Então surge a vontade de saber como essa área de pesquisa se desenvolveu até chegar a esse ponto ou quais desdobramentos ocorreram a partir dessa solução proposta para identificar o estado da arte nesse tema. Podemos seguir duas abordagens:  realizar uma revisão sistemática usando palavras chaves que melhor caracterizam o tema em bibliotecas digitais de referência para encontrar artigos relacionados ou realizar snowballing ancorado nesse artigo que identificamos previamente, explorando os artigos citados (backward) ou os artigos que o citam (forward)  Mas a ferramenta Connected Papers propõe uma abordagem alternativa para essa busca. O problema inicial é dado um artigo de interesse, precisamos encontrar outros artigos relacionados de "certa forma". Find different methods and approaches to the same subject Track down the state of the art rese...

Knowledge Graphs as a source of trust for LLM-powered enterprise question answering - Leitura de Artigo

J. Sequeda, D. Allemang and B. Jacob, Knowledge Graphs as a source of trust for LLM-powered enterprise question answering, Web Semantics: Science, Services and Agents on the World Wide Web (2025), doi: https://doi.org/10.1016/j.websem.2024.100858. 1. Introduction These question answering systems that enable to chat with your structured data hold tremendous potential for transforming the way self service and data-driven decision making is executed within enterprises. Self service and data-driven decision making in organizations today is largly made through Business Intelligence (BI) and analytics reporting. Data teams gather the original data, integrate the data, build a SQL data warehouse (i.e. star schemas), and create BI dashboards and reports that are then used by business users and analysts to answer specific questions (i.e. metrics, KPIs) and make decisions. The bottleneck of this approach is that business users are only able to answer questions given the views of existing dashboa...

Knowledge Graph Toolkit (KGTK)

https://kgtk.readthedocs.io/en/latest/ KGTK represents KGs using TSV files with 4 columns labeled id, node1, label and node2. The id column is a symbol representing an identifier of an edge, corresponding to the orange circles in the diagram above. node1 represents the source of the edge, node2 represents the destination of the edge, and label represents the relation between node1 and node2. >> Quad do RDF, definir cada tripla como um grafo   KGTK defines knowledge graphs (or more generally any attributed graph or hypergraph ) as a set of nodes and a set of edges between those nodes. KGTK represents everything of meaning via an edge. Edges themselves can be attributed by having edges asserted about them, thus, KGTK can in fact represent arbitrary hypergraphs. KGTK intentionally does not distinguish attributes or qualifiers on nodes and edges from full-fledged edges, tools operating on KGTK graphs can instead interpret edges differently if they so desire. In KGTK, e...