If you've ever tried to stay current in a fast-moving research field, you know the problem: there's too much being published to read everything, but missing key papers means missing critical context. You end up doing what researchers have always done—manually searching Google Scholar, reading abstracts, following citation trails, and hoping you find the important work before it's obsoleted by the next breakthrough.
What if you automated that entire workflow? What if you could systematically extract papers, analyze their citation networks, identify the most influential authors and venues, and automatically classify emerging vs. established research?
That's the power of a research pipeline. Let's build one.
The Research Pipeline Architecture
A complete research system has five stages:
Stage 1: Query and Extraction Search for papers matching your research interest. Collect metadata: title, authors, publication date, abstract, citation count.
Stage 2: Retrieval and Enrichment Get the full citation details for each paper. Extract references cited by each paper. Build a bidirectional citation graph.
Stage 3: Classification Categorize papers by research area, methodology, or stage of maturity (foundational vs. incremental vs. applied).
Stage 4: Network Analysis Identify key papers (high in-degree citations), influential authors (frequently cited across papers), core venues (conferences/journals where key work is published).
Stage 5: Trend Detection Compare recent papers vs. older papers. Which topics are accelerating? Which are becoming established? Which are declining?
Let's work through each stage with practical code.
Stage 1: Query and Extraction
The Google Scholar Scraper is your foundation. Configure it to search for your research topic and capture all papers meeting your criteria.
Sample extracted data:
{
"papers": [
{
"title": "Attention Is All You Need",
"authors": ["Ashish Vaswani", "Noam Shazeer", "Parmar N.", "Uszkoreit J."],
"publication_year": 2017,
"venue": "NIPS 2017",
"abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.",
"citation_count": 84320,
"pdf_url": "https://arxiv.org/pdf/1706.03762.pdf",
"google_scholar_url": "https://scholar.google.com/scholar?q=Attention+Is+All+You+Need"
},
{
"title": "Language Models are Unsupervised Multitask Learners",
"authors": ["Tom B. Brown", "Benjamin Mann", "Nick Reiley"],
"publication_year": 2019,
"venue": "OpenAI Blog",
"abstract": "Recent work has demonstrated that transfer learning can greatly improve performance on natural language tasks. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of Internet text...",
"citation_count": 34290,
"pdf_url": "https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf"
},
{
"title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
"authors": ["Jacob Devlin", "Ming-Wei Chang", "Kenton Lee", "Kristina Toutanova"],
"publication_year": 2018,
"venue": "NAACL 2019",
"abstract": "We introduce BERT, a new method of pre-training language representations that obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks.",
"citation_count": 67450,
"pdf_url": "https://arxiv.org/pdf/1810.04805.pdf"
}
],
"metadata": {
"search_query": "transformer language models",
"total_results": 1247,
"results_extracted": 3,
"extraction_date": "2026-04-05T10:30:00Z"
}
}
Stage 2: Citation Network Construction
Here's the critical part. For each paper, you need to extract what it cites and what cites it. This creates a citation graph where nodes are papers and edges are citation relationships.
import json
from collections import defaultdict
from datetime import datetime
class CitationNetworkBuilder:
def __init__(self):
self.papers = {} # paper_id -> paper_data
self.citations = defaultdict(list) # paper_id -> list of cited paper_ids
self.cited_by = defaultdict(list) # paper_id -> list of papers citing it
def add_paper(self, paper_data):
"""Store paper metadata"""
paper_id = self._create_paper_id(paper_data['title'])
self.papers[paper_id] = {
'title': paper_data['title'],
'authors': paper_data['authors'],
'year': paper_data['publication_year'],
'venue': paper_data['venue'],
'citation_count': paper_data.get('citation_count', 0),
'abstract': paper_data.get('abstract', '')
}
return paper_id
def add_citation(self, citing_paper_id, cited_paper_id):
"""Record that paper A cites paper B"""
self.citations[citing_paper_id].append(cited_paper_id)
self.cited_by[cited_paper_id].append(citing_paper_id)
def get_influential_papers(self, min_citations=5):
"""Find papers cited by many others in the network"""
influential = []
for paper_id, citing_papers in self.cited_by.items():
in_degree = len(citing_papers)
if in_degree >= min_citations:
influential.append({
'paper_id': paper_id,
'title': self.papers[paper_id]['title'],
'in_degree': in_degree,
'year': self.papers[paper_id]['year'],
'cited_by': citing_papers[:5] # First 5 citers
})
return sorted(influential, key=lambda x: x['in_degree'], reverse=True)
def get_influential_authors(self, top_n=10):
"""Find authors whose work is most cited in the network"""
author_citation_score = defaultdict(int)
for paper_id, citing_count in self.cited_by.items():
if paper_id in self.papers:
authors = self.papers[paper_id]['authors']
score = len(citing_count) # How many papers in our network cite this
for author in authors:
author_citation_score[author] += score
top_authors = sorted(
author_citation_score.items(),
key=lambda x: x[1],
reverse=True
)[:top_n]
return [
{'author': name, 'network_citation_score': score}
for name, score in top_authors
]
def get_key_venues(self):
"""Identify conferences/journals where influential papers are published"""
venue_scores = defaultdict(int)
for paper_id, in_degree in self.cited_by.items():
if paper_id in self.papers and in_degree:
venue = self.papers[paper_id]['venue']
venue_scores[venue] += in_degree
return sorted(
venue_scores.items(),
key=lambda x: x[1],
reverse=True
)
def classify_paper_maturity(self, paper_id):
"""Classify paper as foundational, core, or emerging"""
paper = self.papers[paper_id]
in_degree = len(self.cited_by[paper_id])
years_published = datetime.now().year - paper['year']
# Heuristic: old papers with high citations are foundational
# New papers with any citations are emerging
# Middle ground are core
if years_published >= 5 and in_degree >= 10:
return 'Foundational'
elif years_published <= 2 and in_degree >= 1:
return 'Emerging'
elif in_degree >= 3:
return 'Core'
else:
return 'Peripheral'
def _create_paper_id(self, title):
"""Create deterministic ID from title"""
return title.lower().replace(' ', '_')[:50]
# Usage example
network = CitationNetworkBuilder()
# Add papers from your Google Scholar export
papers_data = json.load(open('google_scholar_papers.json'))
for paper in papers_data['papers']:
paper_id = network.add_paper(paper)
# Simulate citation relationships (in reality, you'd extract these from papers)
# This would come from parsing paper PDFs or Google Scholar citation links
network.add_citation('attention_is_all_you_need', 'bert_pre_training_of_deep_bidirectional')
network.add_citation('language_models_are_unsupervised', 'attention_is_all_you_need')
network.add_citation('bert_pre_training_of_deep_bidirectional', 'attention_is_all_you_need')
# Analyze the network
influential = network.get_influential_papers(min_citations=1)
top_authors = network.get_influential_authors()
key_venues = network.get_key_venues()
print("Most Influential Papers in Your Research Area:")
for paper in influential[:5]:
print(f" {paper['title']} ({paper['year']}) - cited {paper['in_degree']} times")
print("\nMost Influential Authors:")
for author, score in top_authors[:5]:
print(f" {author} - score: {score}")
print("\nKey Venues:")
for venue, score in key_venues[:5]:
print(f" {venue} - score: {score}")
Stage 3: Paper Classification
Not all papers are equally important for your understanding. Some are seminal foundational work. Some are recent applications. Some are incremental extensions.
Classify papers automatically based on multiple signals:
def classify_paper(paper_data, network_context):
"""Multi-factor paper classification"""
title = paper_data['title'].lower()
abstract = paper_data.get('abstract', '').lower()
year = paper_data['publication_year']
citations = paper_data.get('citation_count', 0)
# Topic classification
if any(term in title + abstract for term in ['survey', 'review', 'overview']):
topic_type = 'Survey'
elif any(term in title + abstract for term in ['benchmark', 'dataset', 'corpus']):
topic_type = 'Resource'
elif any(term in title + abstract for term in ['application', 'implementation', 'case study']):
topic_type = 'Application'
elif any(term in title + abstract for term in ['novel', 'new', 'method', 'algorithm']):
topic_type = 'Method'
else:
topic_type = 'General'
# Maturity classification
years_old = datetime.now().year - year
if years_old >= 5:
maturity = 'Established'
elif years_old <= 1:
maturity = 'Emerging'
else:
maturity = 'Established-Recent'
# Impact classification (based on citations)
if citations >= 1000:
impact = 'Landmark'
elif citations >= 100:
impact = 'High-Impact'
elif citations >= 20:
impact = 'Moderate-Impact'
else:
impact = 'Low-Impact'
return {
'topic_type': topic_type,
'maturity': maturity,
'impact': impact,
'citations': citations,
'year': year
}
# Classify your papers
papers = json.load(open('google_scholar_papers.json'))['papers']
classifications = []
for paper in papers:
classification = classify_paper(paper, network)
classifications.append({
'title': paper['title'],
'classification': classification
})
# Analyze distribution
from collections import Counter
maturity_dist = Counter([c['classification']['maturity'] for c in classifications])
print("Paper Maturity Distribution:", dict(maturity_dist))
impact_dist = Counter([c['classification']['impact'] for c in classifications])
print("Paper Impact Distribution:", dict(impact_dist))
Stage 4 & 5: Trend Analysis
Now the real intelligence emerges. Analyze trends across your research area:
def analyze_trends(papers):
"""Identify what's accelerating, what's established, what's declining"""
by_year = defaultdict(lambda: {'count': 0, 'avg_citations': 0, 'papers': []})
for paper in papers:
year = paper['publication_year']
by_year[year]['count'] += 1
by_year[year]['avg_citations'] += paper.get('citation_count', 0)
by_year[year]['papers'].append(paper['title'])
# Calculate averages
for year in by_year:
count = by_year[year]['count']
if count > 0:
by_year[year]['avg_citations'] = int(by_year[year]['avg_citations'] / count)
# Sort by year
sorted_years = sorted(by_year.items())
# Calculate growth rates
trends = []
for i in range(1, len(sorted_years)):
prev_year, prev_data = sorted_years[i-1]
curr_year, curr_data = sorted_years[i]
growth = ((curr_data['count'] - prev_data['count']) / prev_data['count'] * 100
if prev_data['count'] > 0 else 0)
trends.append({
'year': curr_year,
'papers_published': curr_data['count'],
'yoy_growth': f"{growth:+.1f}%",
'avg_citations_per_paper': curr_data['avg_citations']
})
return trends
trends = analyze_trends(papers)
print("Research Trend Analysis:")
for trend in trends[-5:]: # Last 5 years
print(f" {trend['year']}: {trend['papers_published']} papers " +
f"(YoY growth: {trend['yoy_growth']}) " +
f"avg citations: {trend['avg_citations_per_paper']}")
# Identify emerging topics
recent_papers = [p for p in papers if p['publication_year'] >= datetime.now().year - 2]
recent_keywords = extract_keywords(recent_papers)
older_keywords = extract_keywords([p for p in papers if p['publication_year'] < datetime.now().year - 5])
emerging = set(recent_keywords) - set(older_keywords)
declining = set(older_keywords) - set(recent_keywords)
print("\nEmerging Topics:", emerging)
print("Declining Topics:", declining)
Putting It All Together
Here's your complete workflow:
Week 1 : Set up the Google Scholar Scraper to monitor your research area
- Configure search queries for your field
- Extract all papers matching your criteria
- Store results as JSON
Week 2-3 : Build the citation network
- For each paper, extract references (manual parsing or use citation APIs)
- Build the citation graph
- Identify influential papers and authors
Week 4 : Classify and analyze
- Classify papers by type, maturity, impact
- Analyze trends
- Identify emerging topics
Ongoing : Run weekly or monthly
- Re-run the scraper for new papers
- Update citation counts
- Track trend changes
Practical Use Cases
Once your pipeline is built, you can:
Stay Ahead of Your Field
- Set alerts for when a new influential paper is published
- Know when your domain shifts before competitors do
- Identify which authors to follow for emerging trends
Inform Product Development
- Product team wants to know what's technically feasible? Check if there's recent published work.
- Are you solving a solved problem? The citation network tells you.
- What's actually novel in your approach? Compare against foundational and recent work.
Build Competitive Intelligence
- Which research venues are competitors focused on?
- What problems is the academic community solving that might become products?
- Which authors are most influential in your space?
Research Direction
- Where are the gaps in published work? (Low citation count despite relevance)
- Which methods are becoming standard vs. exploratory?
- What adjacent fields should you be monitoring?
Getting Started
Use the Google Scholar Scraper to extract papers systematically. Set it up to monitor your research area continuously. Then build the analysis layers on top—citation networks, classifications, trend analysis.
The advantage of automating this process is that you see patterns humans miss. After running this pipeline for 3-6 months, you'll have better market intelligence about your research domain than most researchers who are doing this manually.
Start with your core research area. Run the pipeline. Let the data patterns guide your next steps.
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