Network Analysis and Visualization – IT Computer Science Assignment Help

Assignment Task


Network analysis and visualization

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Evaluation Criteria
Students should have a publishable or near-publishable report for their projects. The report may include abstract, introduction, related work, dataset, methods (e.g., algorithms or network metrics used), results (e.g., experimental report, analysis and visualization), and conclusion.

The project will be evaluated based on:

  • The technical quality of the work: does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?
  • Significance: Did the authors choose an interesting or a “real” problem to work on, or only a small “toy” problem? Is this work likely to be useful and/or have impact?
  • The novelty of the work, and the clarity of the write-up
  • Presentation of the results. Well-formatted, well-organised, spell-checked and grammar-checked documents
  • Plagiarism check
  • Members in the same group will receive equal marks. If some of the group feel that other members are not contributing, the instructor should be informed and a group meeting should be held to produce a solution 4 weeks before the deadline. No complaints about group operation will be considered after the project has been handed in.

Selected Topic 1: Friend Recommendation on Social Networks

Social networks are usually highly dynamic; they grow and change quickly over time through the addition of new edges and the removal of old ones. Identifying the mechanisms by which they evolve over time is a fundamental question. In this project, we focus on the link prediction problem on evolving social networks, which aims to predict the future links between nodes by utilizing node features and network features. Let’s take the Facebook “People You May Know” feature as an example. Facebook periodically recommends new people to users such that users can make more new friends.

You may wonder how Facebook recommends friends to you. Are these people just randomly selected, or do they have many common places with you? Actually, Facebook follows the simple intuition that “similar” users are more likely to get connected in real life than the “dissimilar” ones, and thus should be recommended to each other. Following this idea, Facebook recommendation is achieved by mining the implicit online relationships between users, which might finally lead to offline friendship in the future. For example, if two people have lots of common friends, live in the same city or go to the same university, they are very likely to be friends in the future. In this project, you need to investigate various features that may contribute to the connection between two people by exploring network structures. 

A dataset will be provided for this project. We will provide two graph snapshot. The old snapshot is used for algorithm training while the new one is used for evaluation. Astro Physics collaboration network dataset Arxiv ASTRO-PH (Astro Physics) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to Astro Physics category.
If an author i co-authored a paper with author j, the graph contains an undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. The data covers papers in the period from January 1993 to April 2003 (124 months). It begins within a few months of the inception of the arXiv, and thus represents essentially the complete history of its ASTRO-PH section.
The data file contains 18772 nodes (i.e., authors) and 198110 edges (i.e., collaborations). Each line of the data file contains two values representing an edge. The first value is the fromNodeId, and the second value is the toNodeId. 

The evaluation method can be found in the paper “The link prediction problem for social networks” as shown in the reference. Basically, it counts the intersection of the predicted friends and true friends. The higher the counter is, the better the prediction result is. 

Selected Topic 2?Movie Recommendation

Recommendation system is used to predict the “rating” or “preference” that a user would give to an item. Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas such as movie recommendation. In this project, you will be given MovieLens dataset which includes the information of movies and users and the rating a user gives to a movie. Then you can base on that build a simple recommendation system to predict which movies a user may like and predict the rate the user would give to a movie.

MovieLens dataset will be provided for this project. The data was collected through the MovieLens web site ( during the seven-month period from September 19th, 1997 through April 22nd, 1998. The dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. This data has been cleaned up – users who had less than 20 ratings or did not have complete demographic information were removed from this data set.

The data set has 80%/20% split of training data and test data. You can just use the test data to evaluate your result or you can split the data by yourself using the timestamp to evaluate your recommender system. You can evaluate it by searching for the low prediction error (RMSD) and high recall coverage. For details you can click the link in references. In your report you need to give the RMSD and recall of your recommender system.

Selected Topic 3: POI Recommendation

Point-of-interest (POI) recommendation has become a major issue with the rapid emergence of location-based social networks (LBSNs). Unlike traditional recommendation approaches, the LBSNs application domain comes with significant geographical and temporal dimensions.
In this project, you can use the data from Yelp and Foursquare. Let’s take the Yelp data as example. Yelp is one of the most famous LBSNs, and you will be provided the information of the shops and users, e.g. the type of the business, the location of the business, the rate a user gives to a business and the check-in information of a user. Based on this information, you can find what type of a specified user like. For example, one user often goes to Vietnamese restaurant and always rate highly for them, we might have the conclusion that the user likes to eat Vietnamese food and with the location information, you can recommend some nearby Vietnamese restaurant for that user.

You will be provided two datasets for this project. One is from the Yelp Dataset Competition, and the other is from the Foursquare which is also a LBSN. These datasets both contains the common information of business and users and also the location information. You are also encouraged to find dataset which you are interested in. The provide data has following information.  

In this project you might need to predict the rate a user might give to a business and the probability a user check-in in a place. For the Yelp dataset you will need to split some data as test data, you can split it using date, you can use the new rates as test data. You can evaluate your recommender system by searching for the low prediction error (RMSD) and high recall coverage. For details you can click the link in references. In your report you need to give the RMSD and recall of your recommender system.

Selected Topic 4: A Simple Google Search Prototype

The amount of information on the web is growing rapidly every day. Thanks to search engine like Google, users can easily find the information they want through a simple click. Most of the search engines usually return pages of results according to their relevance to the user query. One of the main factors that contribute to Google’s initial success is a ranking model called PageRank. PageRank makes use of the link structure of the web to calculate a quality ranking for each web page. In this project, you need to implement a simple Google search prototype using PageRank. Specifically, given a query, such as “uts Australia”, your search engine should be able to return a set of pages that contain both “uts” and “Australia”, and the most relevant pages should be put on the top. The proposed ranking model should at least use PageRank metric, and the students are encouraged to investigate other features that could be used to improve the ranking.

We will provide a web dataset, named WebSpam. WebSpam contains about 200K web pages as well as their link structures and their raw html contents.
Specifically, we will provide three files:
1. url_graph_file: each node is represented by a unique URL. In this file, every unique URL in the corpus is treated as a node in the web graph, and every unique link to another URL in the corpus is stored as an edge in the web graph.
2. url_id_mapping: maps ids to real URLs.
3. Webspam2011_htl.tgz: the raw html files. You need to match the real URL to the raw html content in order to get the mapping between real URL and its html. 

The evaluation will be based on whether the implemented system can achieve its desired functions. There is no ground truth in this project. 

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