Northeastern University 2014-2015
PHYS 7331: Network Science Data
Lecture hours: Tuesday- Thursday 5-7
Classroom: 160 Richard Hall
Detailed Syllabus: here
Course description and objectives:
The unprecedented amount of data now available in many disciplines changed completely the way we look, understand and study the world and its properties. Network Science provides the new paradigm to analyze and deal with this data deluge. The course will introduce students to mining and analysis techniques in Network Science. The students will learn about working with real world datasets and their description as networks. The course will provide a detailed analysis of algorithms for their characterization and measurement (centrality measures, clustering techniques etc.). Issues in sampling and statistical biases will be presented. Visualization algorithms as well as specific software tools will be reviewed. The course includes hands-on assignments and a final project.
Topics to be covered will include:
Basic mining techniques: from real world datasets to networks:
Homework:
The homework includes:
Written assignment 1 – Centrality measures and community detection
Written assignment 2 – Web mining and dynamics on and of networks
Final project – Written review/discussion on a real network dataset chosen by the students
Examinations: None
Grading
Written assignment 1 – 25 %
Written assignment 2 – 25 %
Final project – 35 %
Attendance – 5%
Teacher evaluation – 10 %
Class policy
Use of laptops in class is allowed only to take notes. In this case please sit in the front rows of the classroom. No email, Facebook, games, or other distractions, please.
Students are responsible for making backups of all of their work. This includes any assignment and other materials you produce.
Students are responsible for the safe and ethical use of class accounts on shared servers, according to university policy and copyright law, and for the sole purpose of carrying out class assignments. Accounts will be monitored and any abuse will be reflected in the grades.
Students are responsible for assigned readings PRIOR to class discussions.
Students are required to attend class.
If you miss class, it is your responsibility to find out about any announcements or assignments you may have missed. Late assignments will incur a penalty of 50% within 24 hours of the deadline, and no partial or make up credit will be available after that.
Extenuating circumstances will normally include only serious emergencies or illnesses documented with a doctor’s note.
Help: If you have trouble with the homework, seek help immediately so that you do not fall behind in the course. You have several places to go for help: your lecturer (after class, during office hours, or anytime by arrangement)
Academic misconduct: Appropriate disciplinary action, potentially including failing the student, will be taken in the event of cheating, plagiarism, dishonesty, or other academic misconduct. The Northeastern University Policy on Academic Integrity can be found at: http://www.northeastern.edu/osccr/code-of-student-conduct/
Since students in this course are often encouraged to work in teams, some specific remarks are in order:
It is not considered cheating if you:
• Work together on homework assignments, as long as you each work out and submit your own final answers
• Get help from professors, physics workshop, tutors, etc. on the homework assignments
• Work together on preparing for quizzes and exams
It is considered cheating if you:
• Submit work done by others (without your participation) as your own
• Copy work on quizzes and exams
Statement of Non Discrimination: Northeastern University is committed to social justice. As the instructor of the course, I expect to maintain a positive learning environment based upon communication and mutual respect. Any suggestions as to how to further such a positive and open environment in this class will be appreciated and given serious consideration. The university does not discriminate on the basis of race, sex, age, disability, religion, sexual orientation, color, or national origin. If you are a person with a disability and anticipate needing any type of accommodation in order to participate in this class, please advise me and make appropriate arrangement with Disability Resource Center (617) 373U4428. The instructor reserves the right to modify this syllabus as deemed necessary any time during the semester. Emendations to the syllabus will be discussed with students during a class period. Students are responsible for information given in class.
There may be also details about the class uncovered in this syllabus. Do not assume something just because it is not specified in the syllabus. If you are unsure about anything related to the rules guiding this course, consult with your instructor.
Lecture hours: Tuesday- Thursday 5-7
Classroom: 160 Richard Hall
Detailed Syllabus: here
Course description and objectives:
The unprecedented amount of data now available in many disciplines changed completely the way we look, understand and study the world and its properties. Network Science provides the new paradigm to analyze and deal with this data deluge. The course will introduce students to mining and analysis techniques in Network Science. The students will learn about working with real world datasets and their description as networks. The course will provide a detailed analysis of algorithms for their characterization and measurement (centrality measures, clustering techniques etc.). Issues in sampling and statistical biases will be presented. Visualization algorithms as well as specific software tools will be reviewed. The course includes hands-on assignments and a final project.
Topics to be covered will include:
Basic mining techniques: from real world datasets to networks:
- Parsing methods starting from different data formats
- Computational techniques to store describe static, directed, weighted, temporal, and multilayered networks
- Basic theory
- Algorithms to compute degree, weights, clustering, shortest paths, activity and their distributions
- Basic theory: non-spectral centrality measures
- Algorithms to compute degree, closeness, and betweenness centrality
- Basic theory: spectral centrality measures
- Algorithms to compute eigenvector, HITS, and PageRank centrality
- Basic theory
- Algorithms for graph partitioning, hierarchical, and spectral clustering
- Divisive, modularity-based, dynamic, and statistical-based algorithms
- Overview on overlapping, and multi-resolution community detection algorithms
- Computational tools: OSLOM, LOUVAIN, INFOMAP
- Basic theory
- Algorithm to evaluate direct, random walk, traceroute-like, and snowball samplings
- Methods to quantify the statistical biases of the different sampling methods
- resolution biases in temporal networks
- Overview on different layouts and their algorithms
- Computational tools: Gephi, networkx, igraph
Homework:
The homework includes:
Written assignment 1 – Centrality measures and community detection
Written assignment 2 – Web mining and dynamics on and of networks
Final project – Written review/discussion on a real network dataset chosen by the students
Examinations: None
Grading
Written assignment 1 – 25 %
Written assignment 2 – 25 %
Final project – 35 %
Attendance – 5%
Teacher evaluation – 10 %
Class policy
Use of laptops in class is allowed only to take notes. In this case please sit in the front rows of the classroom. No email, Facebook, games, or other distractions, please.
Students are responsible for making backups of all of their work. This includes any assignment and other materials you produce.
Students are responsible for the safe and ethical use of class accounts on shared servers, according to university policy and copyright law, and for the sole purpose of carrying out class assignments. Accounts will be monitored and any abuse will be reflected in the grades.
Students are responsible for assigned readings PRIOR to class discussions.
Students are required to attend class.
If you miss class, it is your responsibility to find out about any announcements or assignments you may have missed. Late assignments will incur a penalty of 50% within 24 hours of the deadline, and no partial or make up credit will be available after that.
Extenuating circumstances will normally include only serious emergencies or illnesses documented with a doctor’s note.
Help: If you have trouble with the homework, seek help immediately so that you do not fall behind in the course. You have several places to go for help: your lecturer (after class, during office hours, or anytime by arrangement)
Academic misconduct: Appropriate disciplinary action, potentially including failing the student, will be taken in the event of cheating, plagiarism, dishonesty, or other academic misconduct. The Northeastern University Policy on Academic Integrity can be found at: http://www.northeastern.edu/osccr/code-of-student-conduct/
Since students in this course are often encouraged to work in teams, some specific remarks are in order:
It is not considered cheating if you:
• Work together on homework assignments, as long as you each work out and submit your own final answers
• Get help from professors, physics workshop, tutors, etc. on the homework assignments
• Work together on preparing for quizzes and exams
It is considered cheating if you:
• Submit work done by others (without your participation) as your own
• Copy work on quizzes and exams
Statement of Non Discrimination: Northeastern University is committed to social justice. As the instructor of the course, I expect to maintain a positive learning environment based upon communication and mutual respect. Any suggestions as to how to further such a positive and open environment in this class will be appreciated and given serious consideration. The university does not discriminate on the basis of race, sex, age, disability, religion, sexual orientation, color, or national origin. If you are a person with a disability and anticipate needing any type of accommodation in order to participate in this class, please advise me and make appropriate arrangement with Disability Resource Center (617) 373U4428. The instructor reserves the right to modify this syllabus as deemed necessary any time during the semester. Emendations to the syllabus will be discussed with students during a class period. Students are responsible for information given in class.
There may be also details about the class uncovered in this syllabus. Do not assume something just because it is not specified in the syllabus. If you are unsure about anything related to the rules guiding this course, consult with your instructor.
