About the Graph Convolution-Based Identification of Influential Users in Social Networks project
Graph Convolution-Based Identification of Influential Users in Social Networks studies social networks as graphs. A graph convolutional network learns from both the features of each user and the structure of connections to identify who has the most influence.
In this build: A graph convolutional network learns from the social graph to rank which users are most influential.
ProjectNet offers Graph Convolution-Based Identification of Influential Users in Social Networks as a Java final year project for B.E / B.Tech (CSE, IT), BCA, MCA, M.Tech and Diploma in Computer Engineering. It comes with source code, a project report, a PPT and an installation guide, so you can set it up, understand every module and explain it confidently in your review and viva. Techniques named in the title: Graph Convolutional Networks.
Problem statement
In a large social network only a few users shape what the rest see and do, and finding them is essential for marketing, information spread and moderation. Simple counts such as followers miss the structure of the network, so a model that learns from the graph is needed.
Existing system vs proposed system
Existing system
Degree and centrality measures capture limited aspects of influence and ignore node features and higher-order structure.
Proposed system
The proposed system builds a graph from the social data, trains a graph convolutional network on node features and edges and ranks users by predicted influence.
Key features of Graph Convolution-Based Identification of Influential Users in Social Networks
- Graph construction from social-network data
- Graph convolutional network for node scoring
- Ranking of influential users
- Comparison with degree and PageRank baselines
- Graph visualisation
- Result report
System modules
| Module | What it does |
|---|---|
| Data and graph builder | Converts users and relationships into nodes and edges. |
| Feature preparation | Creates node features and labels. |
| GCN model | Implements graph convolution layers. |
| Training and evaluation | Trains the model and measures ranking quality. |
| Influence ranking | Outputs the top influential users. |
| Visualisation | Draws the graph and highlights key nodes. |
How the system works
- Social data is loaded.
- A graph is built.
- Node features are prepared.
- The GCN is trained.
- Users are scored and ranked.
- Results are visualised and compared with baselines.
Technology stack
Typical technologies used for Java projects like this one. The installation guide lists the exact versions used in the build.
| Programming language | Java (JDK 8 or above) |
|---|---|
| Web layer | JSP & Servlets (or Spring) with HTML, CSS and JavaScript |
| Database | MySQL |
| Server | Apache Tomcat |
| Security / cloud components | Java cryptography APIs; cloud or blockchain modules where the topic needs them |
| IDE | Eclipse / NetBeans / IntelliJ IDEA |
Hardware and software requirements
| Processor | Intel Core i3 or above |
|---|---|
| RAM | 4 GB minimum, 8 GB recommended |
| Storage | 5 GB free space |
| Operating system | Windows 10/11, Ubuntu or macOS |
| Software | JDK 8+, Apache Tomcat 8/9, MySQL Server, Eclipse or NetBeans |
Testing and evaluation
Ranking quality (for example precision@k) and comparison with degree and PageRank baselines evaluate the method.
What you get with this project
- Complete source code with comments
- Project report (abstract, literature review, design, implementation, testing, conclusion)
- PPT for your review presentation
- Installation guide to run the project step by step
- Student support through WhatsApp / phone (+91 9688163291)
To order, message ProjectNet on WhatsApp or call +91 9688163291. After you confirm and pay through GPay, PhonePe or Paytm, the files are shared with you.
Applications and future scope
Applications
- Influencer marketing
- Information and rumour control
- Community detection
- Recommendation systems
Future enhancements
- Dynamic graphs that change over time
- Attention-based graph models
- Larger real-world datasets
Viva questions for Graph Convolution-Based Identification of Influential Users in Social Networks
Be ready to answer these in your project review:
- What is a graph convolutional network?
- How is influence defined in influential-user identification?
- How does GCN differ from a normal neural network?
- What baselines did you compare against?
- How do you build features for nodes?
- How would it scale to a very large graph?
Who can use this project?
Graph Convolution-Based Identification of Influential Users in Social Networks is suitable for B.E / B.Tech (CSE, IT), BCA, MCA, M.Tech and Diploma in Computer Engineering. ProjectNet is based in Bangalore, Karnataka and helps students looking for final year projects in Bangalore and other cities, since all files are shared digitally.
Frequently asked questions
What is included with the Graph Convolution-Based Identification of Influential Users in Social Networks project?
Every ProjectNet project is offered with the complete source code, a project report, a PPT and an installation guide.
How can I get the Graph Convolution-Based Identification of Influential Users in Social Networks project?
Contact ProjectNet on WhatsApp at +91 9688163291 or call 9688163291 / 8270827832. After you confirm the project and make the payment through GPay, PhonePe or Paytm, the source code and report are shared with you.
What is the price of the Graph Convolution-Based Identification of Influential Users in Social Networks project?
The listed price is ₹1,800. Please confirm the current price on WhatsApp before ordering.
Which students can use the Graph Convolution-Based Identification of Influential Users in Social Networks project?
It is suitable for B.E / B.Tech (CSE, IT), BCA, MCA, M.Tech and Diploma in Computer Engineering. Use it as a reference to understand, learn from and extend.
Which technologies and software are needed for Graph Convolution-Based Identification of Influential Users in Social Networks?
The typical stack is Java (JDK 8 or above), JSP & Servlets (or Spring) with HTML, CSS and JavaScript, MySQL, Apache Tomcat. The installation guide lists the exact versions and steps.
Which dataset does Graph Convolution-Based Identification of Influential Users in Social Networks use?
Graph projects use a social-network dataset of users and connections. Ask the team which dataset this build uses.