The Ohio State University
Department of Computer Science and Engineering

CSE 5245: Introduction to Network Science  

Spring 2018, TTH 2:20-3:40, DL480


Introduction to Network Science; Global and Local Network Measures; PageRank; Community Discovery  Algorithms; Network Models; Understanding the role of network analysis in Web and Social network applications

Level and Credits

Prerequisites: Please note that inspite of the title word “Introduction” this will be a somewhat advanced class involving paper readings.  Ideally students should be completely comfortable with topics covered in the following courses:

Instructors: Dr. Srinivasan Parthasarathy, DL 691,;

Teaching Assistant: Yu Wang, DL 686,;

Office Hours and Locations: 

Srinivasan Parthasarathy, Thursdays 1-2 PM, Fridays by appointment 

Yu Wang, Tuesdays 1-2 PM; Wednesdays 11-12 (noon), or Mondays by appointment


·       Familiarity with network science as a discipline

·       Mastery over major macro- and micro- metrics used to describe various networks.

·       Mastery over key community discovery algorithms

·       Familiarity with generative models for networks and various network analysis tools.

·       Mastery of the role of network science in WWW and social network applications

Texts (for reading, free for OSU students)

Approximate Reading Plan (to be revised)

Tentative Grading Plan (Subject to revision)

Assignments and Annotated Bibliographies


Midterm I: TBD


Project and Final Presentation:  TBD


Lecture Notes (note I will be using the blackboard liberally)

·       Minwise Hashing (adapted from authors of MMD book, from DM class) – standalone lecture, something we will use later – covered by TA.

·       Lecture 1 (adapted from various authors – citations in slides)

·       Lecture 2

·       Lecture 3 (adapted from various authors)

·       Lecture 4 (community discovery).  Adapted from: .

·       Lecture 5 (graph models)

·       Lecture 6 (an introduction to cascades)


Homework and Lab Assignments: Given the hands-on, problems assigned for this course project grading will be based on effort, novelty, of approach and clarity of analysis. Reports should be concise and to the point and bereft of spelling and grammatical errors. Also a site of interest in general for this class is . You can use any publicly available software for these assignments or choose to implement your own.

1.     Reading and summarization assignment (individual): Read and summarize the following two papers (which follow along with the lectures). Each summary should be about a page in length (11 point font) and should concisely articulate the key points of the paper and a critical reflection of the paper in today’s day and age – applies specifically to the second paper).  See additional notes on what constitutes a good summary below. (Due date: January 30 2018, in class)



2.     Lab assignment (team of two) (Due date: February 4 2018, via submit cmd)


3.     Assignment 3 (individual): All exercises from the Easley-Kleinberg book (Due date: Feb20th in class)

Exercises: 2.5.1, 2.5.2, 3.7.2, 3.7.3, 3.7.4, 3.7.5, 5.6.1, 5.6.2 and 5.6.3

Extra credit: 2.5.3, 5.6.4

4.     Lab assignment (team of two) (Due date March 11 th (extended) 2018, via submit cmd)


Paper Readings and Summaries: Each group will introduce a topic (30 minute presentation followed by 10 minutes of Q&A). We will discuss 2 papers per class. Each group will have to explain and defend what the paper says, as well as present weaknesses and shortcomings as theysee fit. The rest of the class will be expected to contribute to the discussion as well, and there will be some points assigned for class participation. Ideally, criticisms should be constructive in nature, including the identification of alleviating solutions. Once a paper has been discussed in class you will be expected to compile an annotated bibliography covering all eight papers and submit this to me by the end of the semester. This part of the task (annotated bibliography) is an individual assignment and serves as a take-home final exam (to be turned in the week of finals week). The best time to compile this is to do it as soon as possible after the discussion in class. That is when you will have all the points covered in class.    Feedback forms to help you with this process can be downloaded here [The presentation elements of the feedback forms help with peer evaluation].

 Paper Presentation Schedule:

March 27th  Communities in Context

Armin and Mohammed:  Presentation
Device Placement Optimization with Reinforcement Learning

Ben, SK and Ashwati: Presentation
Community Preserving Network Embedding

March 29th  Network and Behavior Characterization + Project updates

Mengxue, Xianxing and Dingkang  Presentation
YouTube live and Twitch: a tour of user-generated live streaming systems

April 3rd Cascades and Influence:

Mohit and Xiaohu:  Presentation
The Importance of Communities for Learning to Influence

Ben, David and Allie:
:  Presentation
Spatio-temporal Structure of US Critical Care Transfer Network

April 5th Interactive Exploration and Network Security

Moniba, Ritesh and Omid
:  Presentation
VIGOR: Interactive Visual Exploration of Graph Query Results

Sam, Yuan and Evan
:  Presentation
Towards Detecting Compromised Accounts on Social Networks

April 10th Recommender Systems and Social Media

Tom Mike and Bob
:  Presentation
Rating Effects on Social News Posts and Comments

Ryan Justin and Danny
:  Presentation
Learning Personalized Preference of Strong and Weak Ties for Social Recommendation


S. Parthasarathy

January 2018