” INFS7450程序 写作、Media Analytics程序 辅导INFS7450 Social Media AnalyticsProject 2 Link PredictionSemester 1, 2020Goal: The purpose of this project is to help students gain practical experiences and understandthe concepts of a popular data mining task – link prediction in social networks.Dataset: In this project, you will be working with a co-author network. The dataset containsthe following files:*training.txt: This file contains training data set for you to develop your predictionmethods. 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 containsvalidation links for you to tune and validate your developed methods.*test.txt: This 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 tosubmit your results.The dataset is available from UQ blackboard. See /Assessment/INFS7450 Project Two.Task Description:The provided co-author network has 5,242 nodes and 11,696 edges. The edges of the wholeprovided 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 randomlyremoved from the complete dataset and 10,000 negative edges val_negative.txt which werebuilt 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 butnot overlapped with E_validation and are unlabeled). Based on the given training and validationsets of the co-author network, you are required to write a program to rank the unlabeled edgesin the test set. For each pair of nodes in the test set, your program should compute a proximityscore. Rank the 10,100 pairs of nodes according to your computed proximity score indescending order and output the Top-100 edges (or pairs of nodes) with the highest proximityINFS7450作业 写作、Media Analytics作业 辅导score.Input: The provided network datasets.Output: The predicted Top-100 edges.Programming Languages:Python and NetworkX are recommended. However, you have your own choices ofpreferred 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 mustfollow the Format of the provided example file – example.txt.4. Name all the submitted files after your student ID. For example, 41234567.zip forthe source code, 41234567.txt for your submitted results and 41234567.pdf foryour 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 yourcodes cannot be executed Or generate the results as reported, the correspondingmarks for the code and results will be deducted. Results Mark = Accuracy * 7where Accuracy = The number of correctly predicted edges/100. Accuracy iscalculated based on your submitted results compared against the ground truth.Please note, the ground truth will not be released until due. You should evaluateyour code by using the validation data set.如有需要，请加QQ：99515681 或邮箱：99515681@qq.com
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