Graphext Recipe for BlueSky Social Graph

gistfile1.txt

cast(ds.Username, {
    "type": "category"
}) => (ds.Username)

cast(ds["Mutual Connections"], {
    "type": "list[category]"
}) => (ds["Mutual Connections"])

link_rows(ds.Username, ds["Mutual Connections"], {
    "weight_column": null
}) => (ds.targets_out, ds.gx_weight_out)

cluster_network(ds.targets_out,
                ds.gx_weight_out) => (ds.graphext_cluster)

extract_node_pagerank(ds.targets_out,
                      ds.gx_weight_out) => (ds.page_rank)

configure_column_metadata(ds.page_rank,
                          {
                              "label": "Page Rank",
                              "description": "Find nodes whose influence extends into wider networks."
                          })

extract_node_degree(ds.targets_out, ds.gx_weight_out, {
    "mode": "all",
    "directed": false,
    "loops": false
}) => (ds.degree)

configure_column_metadata(ds.degree,
                          {
                              "label": "Degree",
                              "description": "Find very connected nodes, popular individuals."
                          })

extract_node_degree(ds.targets_out, ds.gx_weight_out, {
    "mode": "in",
    "directed": true,
    "loops": false
}) => (ds.indegree)

configure_column_metadata(ds.indegree,
                          {
                              "label": "In-degree",
                              "description": "Find very (in)connected nodes, popular individuals."
                          })

extract_node_degree(ds.targets_out, ds.gx_weight_out, {
    "mode": "out",
    "directed": true,
    "loops": false
}) => (ds.outdegree)

configure_column_metadata(ds.outdegree,
                          {
                              "label": "Out-degree",
                              "description": "Find very (out)connected nodes, popular individuals."
                          })

extract_node_betweenness(ds.targets_out,
                         ds.gx_weight_out) => (ds.betweenness)

configure_column_metadata(ds.betweenness,
                          {
                              "label": "Betweenness",
                              "description": "Individuals who influence the flow around a system."
                          })

layout_network(ds.targets_out, ds.gx_weight_out, {
    "gravity": 0.03,
    "avoidHubs": true,
    "scalingRatio": 3
}) => (ds.x, ds.y)

configure_column_visibility(ds.graphext_cluster, {
    "graph": "pinned"
})

configure_column_visibility(ds.targets_out, {
    "graph": "hidden",
    "details": "hidden"
})

configure_column_visibility(ds.gx_weight_out, {
    "graph": "hidden",
    "details": "hidden"
})

configure_column_visibility(ds.x, {
    "graph": "hidden",
    "details": "hidden"
})

configure_column_visibility(ds.y, {
    "graph": "hidden",
    "details": "hidden"
})

## This function creates a new column that generates a profile link for each username in the dataset. The link format is 'https://bsky.app/profile/{$username}', where '{$username}' is replaced by the actual username from the row. The function will iterate through each row and construct the URL accordingly. 
## 
## Assumptions:
## - Model: gpt-4o-mini & Temperature 0.2
derive_column(ds,
              {
                  "script": "const username = row.Username;\nreturn username ? `https://bsky.app/profile/${username}` : null;",
                  "type": "url"
              }) => (ds.profile_link)

configure_node_url(ds.profile_link)

configure_detail_view(ds.Username, ds.profile_link)

configure_column_visibility(ds.Username, {
    "graph": "hidden"
})

configure_column_visibility(ds["Relationship Type"], {
    "details": "hidden",
    "graph": "hidden"
})

configure_column_visibility(ds["Mutual Connections"], {
    "graph": "pinned"
})

configure_columns_order(ds.graphext_cluster,
                        ds["Mutual Connections"],
                        ds.page_rank,
                        ds.Username,
                        ds["Relationship Type"],
                        ds.targets_out,
                        ds.gx_weight_out)

configure_graph_regions(ds.graphext_cluster, {
    "margin": 80
})

configure_node_color(ds.graphext_cluster)

configure_columns_order(ds.Username,
                        ds.profile_link,
                        ds.graphext_cluster,
                        ds.degree,
                        ds.page_rank,
                        ds["Mutual Connections"],
                        {
                            "for": "dataTable"
                        })

configure_rows_order(ds.degree, {
    "order": "descending"
})

configure_tagged_columns(ds, {
    "User Information": [
        "Username",
        "profile_link"
    ],
    "Network Metrics": [
        "page_rank",
        "degree",
        "indegree",
        "outdegree",
        "betweenness"
    ],
    "Graph Position": [
        "x",
        "y"
    ],
    "Cluster Information": [
        "graphext_cluster"
    ],
    "Connections": [
        "Relationship Type",
        "Mutual Connections"
    ]
})

configure_node_title(ds.Username, {
    "margin": 79
})

configure_node_size(ds.degree, {
    "min": 0.04999999999999999,
    "max": 0.19999999999999976
})

create_project(ds)