• Class Number 4233
  • Term Code 3430
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Lexing Xie
  • Class Dates
  • Class Start Date 19/02/2024
  • Class End Date 24/05/2024
  • Census Date 05/04/2024
  • Last Date to Enrol 26/02/2024
SELT Survey Results

Computational Methods for Network Science covers the essentials of using computational approaches to pose and answer social science research problems. In doing so it also covers a selected set of network algorithms in depth. This includes random graph models, homophily and friendship paradox, influence and contagion in networks, markets and network games, network resilence. Furthermore, it also teaches students about the ethics of doing data-driven social science research.
The course equips the students with in-depth knowledge and hands-on experience in working with network data to study social processes at both the individual and aggregate levels. Graduates will be equipped with the technical, theoretical and conceptual skills and knowledge to start a budding career in this field of research.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Demonstrate a thorough understanding of the fundamental principles of using computational approaches to formulate and answer social science questions.
  2. Demonstrate a working understanding in the ethical concerns of data drive analysis and experiments in human behavior.
  3. Apply network analysis algorithms in practical contexts.
  4. Analyze results from network algorithms, and articulate their limitations.
  5. Migrate insights from network analysis and simulation into new data and application scenarios.
  6. Communicate the process of formulating and solving computational social science problems to a team of professionals with computing and/or social sciences.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

Use of Generative AI tools

The use of Generative AI tools is permitted in this course, given that proper citation and prompt are provided, along with a description of how the tool contributed to the assessment item.

Guidelines regarding appropriate citation and use can be found on the ANU library website. Marks will reflect the contribution of the student rather than the contribution of the tool. Future guidance on appropriate use should be directed to the course convenor.

Class Schedule

Week/Session Summary of Activities Assessment
1 Week 1 to 12 are scheduled with lectures, discussions and project presentation.Detailed schedule and a list of resources are on Wattle.
2 Refer to detailed schedule on Wattle course site. Assignment 1 Out
3 Refer to detailed schedule on Wattle course site.
4 Refer to detailed schedule on Wattle course site. Canberra Public Holiday is on the Monday of this week. Due: Assignment 1
5 Refer to detailed schedule on Wattle course site. Assignment 2 Out
6 Refer to detailed schedule on Wattle course site. The Easter break is at the end of this week.
7 Refer to detailed schedule on Wattle course site.
8 Refer to detailed schedule on Wattle course site. Due: Assignment 1
9 Refer to detailed schedule on Wattle course site.
10 Refer to detailed schedule on Wattle course site. Due: Project Proposal
11 Refer to detailed schedule on Wattle course site.
12 Refer to detailed schedule on Wattle course site. Due: Project Report

Tutorial Registration


Assessment Summary

Assessment task Value Learning Outcomes
Assignment 1 20 % 1, 3, 4
Assignment 2 20 % 1, 3, 4
Paper reading and discussion 10 % 1, 2, 3, 5
Project 50 % 1, 2, 3, 4, 5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 20 %
Learning Outcomes: 1, 3, 4

Assignment 1

Conceptual and programming tasks on network representation and algorithms

Assessment Task 2

Value: 20 %
Learning Outcomes: 1, 3, 4

Assignment 2

Conceptual and programming tasks on network representation and advanced algorithms

Assessment Task 3

Value: 10 %
Learning Outcomes: 1, 2, 3, 5

Paper reading and discussion

Read and discuss research papers on networks with the class, the discussions are expected to contain non-trivial insight and critique.

Assessment Task 4

Value: 50 %
Learning Outcomes: 1, 2, 3, 4, 5


Project proposal, report, and presentation that has significant research content.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically submit all written assessment items.

Hardcopy Submission


Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission permitted up to 24 hours after the designated deadline. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof up to one working day after the deadline. Late submission of assessment tasks is not accepted after 1 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Prof Lexing Xie

Research Interests

Machine learning on the social web, intersecting with computational social science, optimisation and computational economics.

Prof Lexing Xie

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions