CPSC 534L: Information Networks 

                  Laks V.S. Lakshmanan

Classes: MW 1:30-3 pm, ICCS 246, Sept.--Dec. 2021.
First lecture: Wednesday, Sept. 8.

Watch for updates.  

Quick Jumps:

Description
Outline
Marking Scheme
Instructions and Tips on Paper Presentations
Guidelines for Speakers and Discussion Leaders
Evaluation of Presentations and Discussion Leadership
Project Deadlines and Deliverables

Assignments and project talk schedule will be made available from the class piazza page. 

Prerequisites: No formal prerequisites, but ... a working knowledge of graphs, algorithms, basic theory, basic data mining, ML, basic DB will be assumed.

Description: Recent years have witnessed a tremendous interest in networked information structures . Examples of such strctures abound and span the spectrum, from one extreme,   online social networks, to homogeneous and heterogeneous information networks consisting, e.g., of people, resources, and their artifacts, to networks of informational and computational resources, to biological networks and  to brain networks.  Conducting analysis and mining on very large networks is an important part of harnessing Big Data. In an apparently different trend, recommender and collaborative tagging/rating/reviewing systems have gained tremendous popularity. In particular, there is growing evidence suggesting that recommendations will serve as a compelling and complementary alternative to search over large repositories of information. Indeed, an integrated paradigm that combines search with recommendations will significantly boost the quality of resource discovery. Knowledge Graphs (Knowledge Bases) have emerged as a complementary paradigm for information/resource discovery: they represent knowledge glaned from unstructured and structured data in the form of (subject, predicate, object) triples, facilitating efficient question answering. An important concern in solving problems of optimization, search, and recommendation, is fairness, which asks whether all groups, including minorities and protected groups, are treated in an equitable way. 

In this course, we will focus on key data mining and computational problems in Information  Networks and Recommender Systems. Our scope will include the technical aspects of modeling, searching, and mining motivated by challenging research problems that arise in their context and on designing and analyzing algorithms for solving those problems. We will also discuss modeling and computational challenges brought forth by fairness considerations. 

We will be using Piazza for all online discussion related to the course as well as for managing all submissions, announcements, and class resources. Sign up here . See CPSC534LHOME for a detailed description of the class format and related information.

Click   here for the Intro slides.  

Outline:

• Intro. to Networks: different perspectives and terminologies, centrality, focus of this course.
• Structural Analysis over Networks:
  • Team Formation.
  • Community detection – General.
  • k-core and k-truss. k-core and k-truss decomposition.
  • k-core/k-truss communities. 
• Infection, Influence & Information Propagation: single and multiple campaigns; revenue max-imization, misinformation (fake news) mitiga-tion, filter bubbles. 
• Recommender Systems: Content-based vs. Col-laborative Filtering; Memory-based vs. Model-based; Recommendations of novel objects;Strategic/Holistic Recommendations. 
• Knowledge Graphs: Construction, Augmenta-tion, Completion, and Attacks. 

   

  Particular attention to algorithmic fairness -- Fair Optimization -- in influence maximization and recommendations.

   

   

Marking Scheme: 

• Homework Assignments: 20%.
• Class Participation: 5%
• Paper Presentation/Discussion and Reports: 30%
• Course Project: 45% (typically teams of 2-3).