INFS7450 Social Media Analytics Project 2 – Link Prediction
Semester 1, 2021
Goal: This project aims to design and implement an effective algorithm to predict social links, which can be used for friend recommendation in social networks. The implemented algorithm will be evaluated on real-life datasets and its performance will be reported. Project details will be included in the assignment handout. Students are required to finish this project individually.
Dataset: In this project, you will be working with a co-author network. The dataset contains the following files:
*training.txt: This file contains training dataset for you to develop your prediction methods. Each line of this file is a link in the network during the training time period. *val_positive.txt and val_negative.txt: This is the validation set. This file contains validation links for you to tune and validate your developed methods.
*test.txt: This is the test set which contains the unlabeled edges to be ranked. *example.txt: This is an example result file. You must follow the format of this file to submit your results.
The dataset is available from UQ blackboard. See /Assessment/INFS7450 Project Two.
Task:
1. Predict the missing links formed in the future. (15 marks)
Overview: The provided co-author network has 5,242 nodes, 11,696 edges, and an average degree of 5.53. The edges of the whole provided co-author network are then split into three parts, which are E_train (11,496 edges), E_validation (including two parts: 100 positive edges in val_positive.txt which were randomly removed from the complete dataset and 10,000 negative edges val_negative.txt which were built at random and not overlapped with E_train and 100 positive edges in E_validation), and E_test (100 positive edges and 10,000 negative edges which were constructed in the same way but not overlapped with E_validation and are unlabeled). The missing 100 positive edges are formed among the core nodes (whose degrees are larger than 3). Based on the given training and validation sets of the co-author network, you are required to write a program to rank the unlabeled edges in the test set. For each pair of nodes in the test set, your program should compute a proximity score. Rank the 10,100 pairs of nodes according to your computed proximity score in descending order and the Top-100 edges (or pairs of nodes) will be compared with the ground truth to compute accuracy. Input: The provided network datasets.
Output: The predicted Top-100 edges.
Requirements: There is no restriction on algorithms, packages.
Programming Languages:
Python and NetworkX are recommended. However, you have your own choices of preferred programming languages including, but not limited to, Python, MATLAB, Java, C, C++, etc.
Deliverables:
Your submission must include the following:
1. A source code file.
2. A report (.pdf). See the given appendix for an example template.
3. A text file of the predicted Top-100 node pairs (edges). The format of this file must
follow the format of the provided example file – example.txt.
4. Name all the submitted files with your student ID. For example, 41234567.zip for
the source code, 41234567.txt for your submitted results and 41234567.pdf for
your report.
5. Submit one archive file with your student number as the file name (e.g.
41234567.zip). Make sure that all the files mentioned above are in the archive file.
Marking criteria (Total marks:15):
• 15 marks = 4 marks (code) + 7 marks (results) + 4 marks (report)
• Your results should be reproducible and your codes should be readable. If your
codes cannot be executed or generate the results as reported, the corresponding
marks for the code and results will be deducted.
• Results Mark will be calculated as follows:
A. If your accuracy >=0.9: 7 marks (full marks)
B. If0.8<=youraccuracy<0.9:6marks
C. If0.7<=youraccuracy<0.8:5marks
D. If 0.6<= your accuracy <0.7: 4 marks
E. If 0.5<= your accuracy <0.6: 3 marks
F. If 0.3<= your accuracy <0.5: 2 marks
G. If 0.1<= your accuracy <0.3: 1 marks
H. If your accuracy <0.1: 0 marks
where “Accuracy = The number of correctly predicted edges/100.” Accuracy is calculated based on your submitted results compared against the ground truth.