Math recitation references:

• Review of probability from a course by David Blei from Princeton U.
• Machine Learning Math Essentials by Jeff Howbert from Washington U.

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

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

To read and present:

• Semi-supervised learning using gaussian fields and harmonic functions. (ICML’03)
• Graph Regularized Transductive Classification on Heterogeneous Information Networks (ECMLPKDD’10)

Other references:

• F. Wang and C. Zhang, "Label Propagation Through Linear Neighborhoods," ICML'06, 2006.

Hanwen Wang, Zeyu Li, and Xinxin Huang

Slides

To read and present:

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

Swati Arora, Twinkle Gupta, and Shikhar Malhotra

Slides

To read and present:

• G. Jen and J. Widom, "SimRank: A measure of structural-context similarity," KDD'02, 2002.
• Fast Single-Pair SimRank Computation (SDM’10)

Other references:

• Cuiping Li, Jiawei Han, Xin Jin, Yizhou Sun, Yintao Yu, and Tianyi Wu, "Fast Computation of SimRank for Static and Dynamic Information Networks", Proc. 2010 Int. Conf. on Extending Data Base Technology (EDBT'10), Lausanne, Switzerland, March 2010.

To read and present:

• (PathSim) "PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks" (VLDB’11)
• Discovering Meta-Paths in Large Heterogeneous Information Networks (WWW’15)

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)

To read and present:

• GloVe: Global Vectors forWord Representation (EMNLP’14)
• Node2Vec: node2vec: Scalable Feature Learning for Networks (KDD’16)

To read and present:

• (LINE) LINE: Large-scale Information Network Embedding. (WWW'15)
• (PTE) PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. (KDD'15)

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)

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)

 To read and present:

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

 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.