Class Schedule

CS 249: Special Topics - Mining Information and Social Networks

News

[01/09/2017] First day of classes


Class Schedule

(Future lectures and events are tentative.)

Week# Date Topic Papers to Read Presenters Course Project
1 Jan. 9 Introduction

Math recitation references:

   
1 Jan. 11 Overview

 

Presentation Sign-up Due  
2 Jan. 16 No Class (Holiday)

 

   
2 Jan. 18 Clustering 1

To read and present:

  • Modularity and community structure in networks. (PNAS’06)
  • Fast algorithm for detecting community structure in networks (arXiv’03)

Amar Chandole,
Ameya Kabre, and
Atishay Aggarwal

Slides

Group Formation Due
3 Jan. 23 Clustering 2

To read and present:

  • Spectral methods for network community detection and graph partitioning (arXiv’13)

Yunqi Guo, Xueyin Yu, and Yuanqi Li

Slides

Derivations

Example

 
3 Jan. 25 Classification 1

To read and present:

Other references:

Hanwen Wang, Zeyu Li, and Xinxin Huang

Slides

 
4 Jan. 30 Classification 2

To read and present:

  • Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction (UAI’13)

Swati Arora, Twinkle Gupta, and Shikhar Malhotra

Slides

 
4 Feb. 1 Similarity Search 1

To read and present:

Other references:

  Proposal Due (Feb. 3)
5 Feb. 6 Similarity Search 2

To read and present:

   
5 Feb. 8 Guest Lecture  Embedding  Ting Chen  
6 Feb. 13 Embedding 1

To read and present:

  • (Word2Vec) Distributed Representations of Words and Phrases and their Compositionality (NIPS’13)
  • (DeepWalk) DeepWalk: Online Learning of Social Representations (KDD’14)
   
6 Feb. 15 Embedding 2

To read and present:

  • GloVe: Global Vectors forWord Representation (EMNLP’14)
  • Node2Vec: node2vec: Scalable Feature Learning for Networks (KDD’16)
   
7 Feb. 20 No Class (Holiday)

 

   
7 Feb. 22 Embedding 3

To read and present:

   
8 Feb. 27 Embedding 4

To read and present:

  • (TransE) Translating Embeddings for Modeling Multi-relational Data. (NIPS’13)
  • (TransH) Knowledge Graph Embedding by Translating on Hyperplanes. (AAAI’14)
  • (TransR) Learning Entity and Relation Embeddings for Knowledge Graph Completion. (AAAI’15)
   
8 Mar. 1 K-Core Decomposition

To read and present:

  • Large scale networks fingerprinting and visualization using the k-core decomposition (NIPS’05)
  • CoreScope: Graph Mining Using k-Core Analysis (ICDM’16)
   
9  Mar. 6  Influence Maximization

 To read and present:

  • Maximizing the Spread of Influence through a Social Network (KDD’03)
  • Efficient Influence Maximization in Social Networks (KDD’09)
   
9  Mar. 8  Recommendation

 To read and present:

  • Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," KDD’10, 2010.
  • Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han, "Personalized Entity Recommendation: A Heterogeneous Information Network Approach," WSDM'14, 2014.
   
10 Mar. 13 Project Presentation      
10 Mar. 15 Project Presentation      
11 Mar. 20 No Class     Final Report Due