Construct the graph
Load a citation dataset (for example Cora) into a NetworkX or PyG graph with nodes, directed edges and available node features.
01 / THE IDEA
Citation Network Analysis in practice.
Citation network analysis represents papers as nodes and citation relations as edges to study influence, communities and recommendation tasks. Common modelling steps include extracting node features (citation counts, PageRank), designing a prediction objective (node classification, link prediction or ranking), and choosing evaluation splits that avoid leakage across connected components.
Real-world use case · Proposed extension
A proposed extension: help research librarians or analysts identify influential or emerging papers by ranking nodes with explainable centrality features. Any operational use should keep a human reviewer in the loop and treat scores as advisory rather than definitive.
02 / THE BUILD
How to build a citation network analysis project
step by step.
Compute baseline features
Calculate degree, clustering coefficient and PageRank as simple, interpretable features for each paper before modelling.
Define a prediction or ranking task
Choose node classification, link prediction or a ranking objective and create splits that avoid leakage across connected components.
Plan production extensions
Replace baseline features with a GNN (PyTorch Geometric/DGL), add monitoring for drift and expose predictions through a reviewable dashboard.
03 / SOURCE SNAPSHOT
What the citation network analysis starter actually does.
The supplied starter builds NetworkX's karate_club_graph, computes node-level degree, clustering and PageRank, creates a combined 'risk_or_class_score' as a weighted rank-average of those features, and prints the top-ranked nodes. It does not use a citation dataset or train a graph neural network; it computes descriptive centrality-based scores on a small social graph.
SOURCE EXCERPTPython
graph = nx.karate_club_graph()
features = pd.DataFrame(
{
"node": list(graph.nodes()),
"degree": [graph.degree(node) for node in graph.nodes()],
"clustering": [nx.clustering(graph, node) for node in graph.nodes()],
"pagerank": [nx.pagerank(graph)[node] for node in graph.nodes()],
}Important limitation
The starter uses the built-in Karate Club social graph rather than a citation dataset like Cora or PubMed, and it computes a deterministic, hand-weighted score rather than training a graph neural network or evaluating a predictive task. The small synthetic graph and ad-hoc scoring cannot validate citation-analysis performance.
04 / BEYOND THE CLASSROOM
Where a citation network analysis project could go next.
PROPOSED BUSINESS CONCEPT
Citation network analysis: a pilot concept
A proposed extension is to swap the starter graph for a real citation dataset, train a GNN for node classification or link prediction, and expose ranked results in a reviewable interface for research analysts.
WHO IT COULD SERVEResearch labs and library analysts
FIRST MVPA small service that ingests a citation graph, computes baseline centrality features and GNN predictions, displays ranked papers with feature explanations, and allows human feedback.
KEY RISKRanking or classification errors may prioritise well-connected but irrelevant papers; biased or incomplete citation data can mislead users, so a human reviewer must validate outputs before any operational decision.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a citation network analysis project puts on your resume.
- Represent citation data as nodes, edges and node features suitable for graph modelling.
- Compute and interpret baseline graph features such as degree, clustering coefficient and PageRank.
- Design evaluation splits for node tasks that reduce leakage across connected components.
- Plan a path from feature-based baselines to a GNN-backed prototype with deployment and monitoring considerations.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Citation Network Analysis project FAQs.
- Is this suitable as a final year project?
- Yes — it makes a strong applied final year project that combines graph analytics and ML concepts; scope can be expanded from feature-engineering to predictive tasks or GNN modelling.
- What do I need to learn or prepare?
- Familiarity with Python, NetworkX, pandas, basic graph theory (centrality, PageRank), and supervised learning; for extensions learn PyTorch Geometric or DGL for GNNs.
- How should I evaluate progress and timeline?
- Start with dataset collection and feature extraction (1–2 weeks), implement ranking/classification baselines (2–3 weeks), then add GNN models and evaluation splits avoiding leakage (3–5 weeks).