General Info: Graduate-level course, CSE Dept., UC San Diego, Spring quarter 2017
Lecture hours: Tuesday and Thursday, 18:30 - 19:50
Lecture room: PCYNH 106
Instructor: Fragkiskos Malliaros
Email: fmalliaros [at] eng.ucsd.edu
Office hours: TBA
TA: Mohammad Motiei
Email: mmotiei [at] eng.ucsd.edu
Office hours: TBA
Piazza: piazza.com/ucsd/spring2017/cse291f/home
Networks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. Social networks, such as academic collaboration networks and interaction networks over online social networking applications are used to represent and model the social ties among individuals. Information networks, including the hyperlink structure of the Web and blog networks, have become crucial mediums for information dissemination, offering an effective way to represent content and navigate through it. A plethora of technological networks, including the Internet, power grids, telephone networks and road networks are an important part of everyday life. The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and analysis methods constitute prominent tools. The goal of this course is to present recent and state-of-the-art methods and algorithms for exploring, analyzing and mining large-scale networks, as well as their practical applications in various domains (e.g., social science, the web, biology).
Week | Date | Topic | Material | Assignments/Project |
---|---|---|---|---|
1 | April 4 | Introduction | Lecture 1 | |
April 6 | Graph theory, linear algebra and ML basics recap | Lecture 2 | ||
2 | April 11 | Network properties and the random graph model | ||
April 13 | Network generative models | |||
3 | April 18 | Random walks and centrality criteria | Assignment 1 out | |
April 20 | Link analysis algorithms (PageRank and HITS) | |||
4 | April 25 | Project proposal short presentations (all teams) | Project proposal slides due on April 24 | |
April 27 | No class. Traveling to SDM 2017 | Project proposal due | ||
5 | May 2 | Graph clustering and community detection (Part I) | Assignment 1 due | |
May 4 | Graph clustering and community detection (Part II) | |||
6 | May 9 | Node similarity and link prediction | Assignment 2 out | |
May 11 | Graph similarity, graph kernels and graph classification | |||
7 | May 16 | Representation learning in graphs | ||
May 18 | Influential spreaders and influence maximization | |||
8 | May 23 | Anomaly detection | Project progress report due | |
May 25 | Graph sampling and summarization | |||
9 | May 30 | Dense subgraph detection and applications | Assignment 2 due | |
June 1 | Rich network structures: signed, uncertain, multilayer graphs, geo-social and textual networks | |||
10 | June 6 | Core decomposition in graphs | ||
June 8 | Project presentations or poster session. The date is subject to change based on student's availability | Project final report due on June 10 |
Prerequisites
There is no official prerequisite for this course. However, the students are expected to:
Reading material
Most of the material of the course is based on research articles. Some of the topics are also covered by the following books:
Evaluation
The evaluation of the course will be based on the following:
Assignment 1 (individual): | 10% |
Assignment 2 (groups of 3-4 students): | 30% |
Project (groups of 3-4 students): | 60% |
Academic integrity
UCSD and CSE's policies on academic integrity will be strictly enforced (please see here and here). In particular, all of your work must be your own. Some relevant excerpts from UCSD's policies are:
Datasets
Software tools