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| import re
import json
from collections import defaultdict, Counter
from itertools import combinations
from pathlib import Path
import argparse
def normalize_author_name(name: str) -> str:
"""
Normalize author names:
- "Smith, John" -> "John SMITH"
- Remove dots and extra spaces
- Capitalize first names, uppercase last name
"""
name = name.replace(".", "").strip()
if "," in name:
parts = [p.strip() for p in name.split(",")]
if len(parts) == 2:
first, last = parts[1], parts[0]
else:
return name
else:
parts = name.split()
if len(parts) >= 2:
first, last = " ".join(parts[:-1]), parts[-1]
else:
return name
first = " ".join(w.capitalize() for w in first.split())
last = last.upper()
return f"{first} {last}"
def parse_bibtex_authors(bib_file: str):
"""
Parse authors from a BibTeX file.
Returns a list of lists: [[author1, author2, ...], ...]
"""
with open(bib_file, encoding="utf-8") as f:
content = f.read()
entries = content.split("@")[1:]
papers_authors = []
for entry in entries:
match = re.search(r"author\s*=\s*[{](.*?)[}]", entry, re.S | re.I)
if not match:
continue
authors_raw = match.group(1)
authors = [
normalize_author_name(a) for a in authors_raw.split(" and ") if a.strip()
]
papers_authors.append(authors)
return papers_authors
def build_coauthor_graph_with_weights(papers_authors):
"""
Build a co-author graph (undirected) with edge weights.
Returns:
- graph: adjacency set dict
- edge_counter: dict with tuple(sorted(a,b)) -> number of coauthored papers
"""
graph = defaultdict(set)
edge_counter = Counter()
for authors in papers_authors:
for a, b in combinations(authors, 2):
graph[a].add(b)
graph[b].add(a)
edge = tuple(sorted([a, b]))
edge_counter[edge] += 1
return graph, edge_counter
def export_to_html(graph, counter, edge_counter, output_file="coauthors.html"):
"""
Export the co-author graph to an HTML file with D3.js force-directed layout.
Edge thickness and label show number of coauthored papers.
"""
nodes = [{"id": a, "group": 1, "size": counter[a]} for a in graph]
links = [
{"source": a, "target": b, "value": edge_counter[tuple(sorted([a, b]))]}
for a in graph
for b in graph[a]
if a < b
]
data = {"nodes": nodes, "links": links}
html_template = f"""
<!DOCTYPE html>
<meta charset="utf-8">
<style>
svg {{
border: 1px solid #ccc;
background-color: #fff;
}}
.links line {{
stroke: #999;
stroke-opacity: 0.6;
}}
.nodes circle {{
stroke: #fff;
stroke-width: 1.5px;
}}
text {{
font-family: sans-serif;
font-size: 10px;
}}
</style>
<body>
<div style="display:flex; flex-direction:column; align-items:center; height:100vh; margin:0;">
<h1 style="margin-bottom:10px;">Co-author Network</h1>
<svg width="960" height="600"></svg>
</div>
<script src="https://d3js.org/d3.v7.min.js"></script>
<script>
var graph = {json.dumps(data)};
var svg = d3.select("svg"),
width = +svg.attr("width"),
height = +svg.attr("height");
var color = d3.scaleOrdinal(d3.schemeCategory10);
var container = svg.append("g");
var link = container.append("g")
.attr("class", "links")
.selectAll("line")
.data(graph.links)
.enter().append("line")
.attr("stroke-width", d => Math.sqrt(d.value)); // thickness by coauthored papers
var linkLabel = container.append("g")
.selectAll("text")
.data(graph.links.filter(d => d.value > 1))
.enter().append("text")
.text(d => d.value)
.attr("font-size", 8)
.attr("fill", "#555");
var node = container.append("g")
.attr("class", "nodes")
.selectAll("circle")
.data(graph.nodes)
.enter().append("circle")
.attr("r", d => 5 + d.size)
.attr("fill", d => color(d.group))
.call(drag(simulation));
var label = container.append("g")
.selectAll("text")
.data(graph.nodes)
.enter().append("text")
.text(d => d.id)
.attr("x", 6)
.attr("y", 3);
node.append("title")
.text(d => d.id);
var simulation = d3.forceSimulation(graph.nodes)
.force("link", d3.forceLink(graph.links).id(d => d.id).distance(80))
.force("charge", d3.forceManyBody().strength(d => -80-40 * Math.sqrt(d.size)))
.force("center", d3.forceCenter(width / 2, height / 2))
.force("x", d3.forceX(width/2).strength(0.05))
.force("y", d3.forceY(height/2).strength(0.05))
.force("collide", d3.forceCollide(d => 5 + d.size + 2));
simulation.on("tick", () => {{
link
.attr("x1", d => d.source.x)
.attr("y1", d => d.source.y)
.attr("x2", d => d.target.x)
.attr("y2", d => d.target.y);
linkLabel
.attr("x", d => (d.source.x + d.target.x)/2 )
.attr("y", d => (d.source.y + d.target.y)/2 );
node
.attr("cx", d => d.x)
.attr("cy", d => d.y);
label
.attr("x", d => d.x + 6)
.attr("y", d => d.y + 3);
}});
function drag(simulation) {{
function dragstarted(event, d) {{
if (!event.active) simulation.alphaTarget(0.3).restart();
d.fx = d.x;
d.fy = d.y;
}}
function dragged(event, d) {{
d.fx = event.x;
d.fy = event.y;
}}
function dragended(event, d) {{
if (!event.active) simulation.alphaTarget(0);
d.fx = null;
d.fy = null;
}}
return d3.drag()
.on("start", dragstarted)
.on("drag", dragged)
.on("end", dragended);
}}
var zoom = d3.zoom()
.scaleExtent([0.1, 4])
.on("zoom", (event) => {{
container.attr("transform", event.transform);
}});
svg.call(zoom);
simulation.on("end", () => {{
const bounds = container.node().getBBox();
const scale = 0.85 / Math.max(bounds.width / width, bounds.height / height);
const translate = [
width / 2 - scale * (bounds.x + bounds.width / 2),
height / 2 - scale * (bounds.y + bounds.height / 2)
];
svg.transition().duration(750).call(
zoom.transform,
d3.zoomIdentity.translate(translate[0], translate[1]).scale(scale)
);
}});
</script>
</body>
"""
output_path = Path(output_file).resolve()
with open(output_path, "w", encoding="utf-8") as f:
f.write(html_template)
print(f"\nVisualization generated.\nOpen in your browser: {output_path.as_posix()}")
def main():
parser = argparse.ArgumentParser(
description="Generate co-author network from a BibTeX file."
)
parser.add_argument("bibfile", help="Path to the input BibTeX file")
parser.add_argument(
"-o", "--output", default="coauthors.html", help="Output HTML file"
)
args = parser.parse_args()
papers_authors = parse_bibtex_authors(args.bibfile)
if not papers_authors:
print("No authors found in the BibTeX file.")
return
counter = Counter([a for authors in papers_authors for a in authors])
print("=== Top 10 authors by number of papers ===")
for author, count in counter.most_common(10):
print(f"{author}: {count} papers")
graph, edge_counter = build_coauthor_graph_with_weights(papers_authors)
export_to_html(graph, counter, edge_counter, args.output)
if __name__ == "__main__":
main()
|
