Helpers

This module contains helper functions for building the network and storing data.

append_pub_data_to_json(publication_info)

Saves publication data from get_publication_data to data/scraped.json file.

Parameters:

Name	Type	Description	Default
publication_info	list	Publication data.	required

Source code in scholar_network/helpers.py

def append_pub_data_to_json(publication_info: list[dict[str, str]]):
    """Saves publication data from
    [get_publication_data][src.scholar_network.scraping.get_publication_data]
    to data/scraped.json file.

    Args:
        publication_info (list[dict[str, str]]): Publication data.
    """
    with open("data/scraped.json", "r", encoding=ENCODING) as f:
        data = json.load(f)
    data.extend(publication_info)
    with open("data/scraped.json", "w", encoding=ENCODING) as f:
        json.dump(data, f, indent=4, sort_keys=True)

build_graph(author1=None, author2=None)

This utility function builds the Graph for the network.

Currently the default graph type that is built is undirected.

If no authors are provided, then a network of all the data contained in data/scraped.json will be built.

If one or two authors are provided, only their networks will be built.

Parameters:

Name	Type	Description	Default
author1	Optional[str]	Author name. Defaults to None.	None
author2	Optional[str]	Author name. Defaults to None.	None

Returns:

Type	Description
Graph	models.Graph: Network graph of authors.

Source code in scholar_network/helpers.py

def build_graph(
    author1: Union[str, None] = None, author2: Union[str, None] = None
) -> models.Graph:
    """This utility function builds the Graph for the network.

    Currently the default graph type that is built is undirected.

    If no authors are provided, then a network of all the data contained in
    `data/scraped.json` will be built.

    If one or two authors are provided, only their networks will be built.

    Args:
        author1 (Union[str, None], optional): Author name. Defaults to None.
        author2 (Union[str, None], optional): Author name. Defaults to None.

    Returns:
        models.Graph: Network graph of authors.
    """
    # TODO: add support for journal title as attribute of edge
    # TODO: want weight to track number of connections on each edge
    # TODO: look at speed enhancements
    # journal = scholars[name].get("journal_title").strip()

    publications = load_publications()
    graph = models.Graph()
    if not author1 and not author2:  # make whole graph
        for pub in publications:
            co_authors = set([c.strip() for c in pub.get("authors").split(",")])

            for first in co_authors:
                n1 = models.Node(first)
                graph.add_node(n1)
                for second in co_authors:
                    if second == first:
                        continue
                    n2 = models.Node(second)
                    graph.add_node(n2)
                    graph.add_edge(
                        models.Edge(n1, n2)
                    )
        return graph
    else:  # otherwise at least one author passed
        for pub in publications:
            co_authors = set([c.strip() for c in pub.get("authors").split(",")])
            # if author in coauthors set then add that network
            if author1 in co_authors or author2 in co_authors:
                for first in co_authors:
                    n1 = models.Node(first)
                    graph.add_node(n1)
                    for second in co_authors:
                        if second == first:
                            continue
                        n2 = models.Node(second)
                        graph.add_node(n2)
                        graph.add_edge(
                            models.Edge(n1, n2)
                        )
        return graph

load_publications()

Utility function to load publication data.

Returns:

Type	Description
list	(list[dict[str, str]]): List of publication data.

Source code in scholar_network/helpers.py

def load_publications() -> list[dict[str, str]]:
    """Utility function to load publication data.

    Returns:
        (list[dict[str, str]]): List of publication data.
    """
    with open("data/scraped.json", "r", encoding=ENCODING) as f:
        scholars = json.load(f)
    return scholars