_pkgdown.yml

destination: docs
url: https://tidygraph.data-imaginist.com

authors:
  Thomas Lin Pedersen:
    href: https://data-imaginist.com

template:
  params:
    bootswatch: journal

navbar:
  left:
    - icon: fa-home fa-lg
      href: index.html
    - text: Reference
      href: reference/index.html
    - text: News
      menu:
      - text: "Release notes"
      - text: "Version 1.3.0"
        href: https://www.data-imaginist.com/2023/a-new-focus-on-tidygraph/
      - text: "Version 1.1.0"
        href: https://www.data-imaginist.com/2018/tidygraph-1-1-a-tidy-hope/
      - text: "Version 1.0.0"
        href: https://www.data-imaginist.com/2017/introducing-tidygraph/
      - text: "------------------"
      - text: "Change log"
        href: news/index.html
  right:
    - text: ggraph
      href: https://ggraph.data-imaginist.com
    - icon: fa-github fa-lg
      href: https://github.com/thomasp85/tidygraph

reference:
  - title: New verbs
    desc: >
      While tidygraph mainly works by allowing you to use well known dplyr verbs
      on relational data, it provides a few new verbs special to working with
      this type of data.
    contents:
      - activate
      - bind_graphs
      - graph_join
      - reroute
      - focus
      - iterate
      - morph
      - to_simple
  - title: Graph creation
    desc: >
      Graphs and networks can come from many sources, or be created by
      simulation or deterministacally. Tidygraph provides conversions from all
      well-known structures in R, as well as a range of `create_()` and
      `play_*()` functions for creating well-defined or simulated graphs.
    contents:
      - tbl_graph
      - create_notable
      - play_degree
      - play_smallworld
      - play_forestfire
      - play_preference
  - title: Mapping over nodes
    desc: >
      Mapping functions over nodes for relational data is different than for
      standard tabular data as you often want to recurse over a search or in
      other ways use the graph structure as part of your map.
    contents:
      - map_bfs
      - map_bfs_back
      - map_dfs
      - map_dfs_back
      - map_local
  - title: Searches
    desc: >
      Searching is central to many graph algorithms and is often performed in
      either a breath-first, or depth-first manner. Tidygraph provides a slew of
      functions for extracting out information from such searches.
    contents:
      - search_graph
  - title: Centrality
    desc: >
      Centrality is a key measure in network analysis, and while it is mainly
      calculated for nodes, a few algorithms exists for calculating edge
      centrality as well.
    contents:
      - centrality_alpha
  - title: Community detection
    desc: >
      Social network analysis is especially interested in detecting groups or
      communities within a graph, but such algorithms are also useful in other
      areas of network research. No single algorithm can provide the correct
      grouping of nodes so several exists that weigh certain features
      differently.
    contents:
      - group_components
  - title: Node measures
    desc: >
      Tidygraph provides a wide range of algorithms for extracting differnt
      information about the nodes in a graph, from calculating local measures,
      to defining whether they are part of certain topological features.
    contents:
      - local_size
      - node_constraint
      - node_rank_hclust
      - node_dominator
      - node_is_cut
      - node_adhesion_to
      - random_walk_rank
  - title: Edge measures
    desc: >
      Fewer quantitative measures exist for edges, but they can still be queried
      for topological features such as which nodes they link, etc.
    contents:
      - edge_is_mutual
      - edge_rank_eulerian
      - random_walk_rank
  - title: Graph measures
    desc: >
      The graph under investigation can also have properties of its own that can
      be summarised into quantitative or qualitative values.
    contents:
      - graph_is_simple
      - graph_radius
  - title: Misc
    desc: >
      Outside of the verbs and algorithms there is a few helper functions
      available to streamline graph manipulation.
    contents:
      - .N
      - with_graph