CMP615 GRAPH MINING AND NETWORK ANALYSIS

CMP615 GRAPH MINING AND NETWORK ANALYSIS

SPRING 2024

INSTRUCTOR: Engin Demir

LECTURES: Thu 9:30-12:00

COURSE DESCRIPTION: The course covers recent research on the structure and analysis of such large networks and on models and algorithms that abstract their basic properties. Main topics to be explored are how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution

Students are expected to (1) have basic knowledge of linear algebra, machine learning (2) be familiar with probability theory and statistics, and (3) have good Python programming skills

REFERENCE BOOKS:

Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg
Network Science by Albert-László Barabási
Social Media Mining: An Introduction by Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu
Graph Representation Learning by William L. Hamilton
Mining of Massive Datasets by Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman.
Mark Newman. 2010. Networks: An Introduction. Oxford University Press
Eric D. Kolaczyk. 2009. Statistical Analysis of Network Data: Methods and Models. Springer Publishing Company, Incorporated

GRADING POLICY:

Practical (paper reproducability)	40
Project	60

COMMUNICATION:

The course webpage will be updated regularly throughout the semester with lecture notes, programming and reading assignments and important deadlines. All other communications will be carried out through Piazza. Please enroll it by following the links http://www.piazza.com/hacettepe.edu.tr/spring2024/cmp615

SCHEDULE

Weeks

Topics

1
Structure of Graphs, Measuring Networks, and Random Graph Model

2
Link Analysis: PageRank, HITS

3
Network Construction, Inference, and Deconvolution

4
Motifs and Graphlets

5
Community Structure in Networks & Community Detection: Spectral Clustering

6
Link Prediction

7
Graph Representation Learning

8
Network Effects and Cascading Behavior

9
Influence Maximization & Outbreak Detection

10
Power-laws and Network Robustness

11
Network Centrality

12
Message Passing and Node Classification

13
Network Evolution

14
Poster session (Project)

Weeks	Topics
1	Structure of Graphs, Measuring Networks, and Random Graph Model
2	Link Analysis: PageRank, HITS
3	Network Construction, Inference, and Deconvolution
4	Motifs and Graphlets
5	Community Structure in Networks & Community Detection: Spectral Clustering
6	Link Prediction
7	Graph Representation Learning
8	Network Effects and Cascading Behavior
9	Influence Maximization & Outbreak Detection
10	Power-laws and Network Robustness
11	Network Centrality
12	Message Passing and Node Classification
13	Network Evolution
14	Poster session (Project)