Time  Topic  Contents  Presenter 
01/14  Introduction  (1) Motivation (2) Syllabus and grading policy (3) Random graphs (4) Paper presentations  Jianzhu Ma 
01/16  Network Visualization  (1) Cytoscape (2) HiView (3) NDEx  Jianzhu Ma 
01/21  Introduction to Deep Learning  (1) Basic concepts, MLP (2) CNN (3) RNN (4) LSTM (5) Resnet (6) GNN  Jianzhu Ma 
01/23  Network Motifs  (1) Network motifs in biological networks (2) Gtries algorithm  Jianzhu Ma 
01/28  PageRank  (1) PageRank algorithm (2) Personalized PageRank and Random Walk algorithm (3) Random Walk in Cancer  Jianzhu Ma 
01/30  Network Community Detection I  (1) BronKerbosch algorithm (2) KernighanLin algorithm (3) Louvain algorithm  Jianzhu Ma 
02/04  Network Community Detection II  (1) Spectual clustering (2) Spectual modularity maximization (3) Hierarchical graph clustering Optional reading: Awesome community detection algorithms [github]  Jianzhu Ma 
02/06  Network Alignment  (1) PathBLAST (2) IsoRank (3) Representationbased network alignments Optional Reading: (1) REGAL: Representation Learningbased Graph Alignment [pdf] (2) Deep Adversarial Network Alignment [pdf]  Jianzhu Ma 
02/11  Structural Roles in Network  (1) Roles and Communities (2) ReFeX (3) RolX Optional Reading: (1) From Community to Rolebased Graph Embeddings [pdf] (2) Role Discovery in Networks [pdf] (3) Introduction to social network methods Chapter 14 [url]  Jianzhu Ma 
02/13  Graph Summarization  (1) Graph Dedensification (2) Vocabularybased Summarization (3) SlashBurn algorithm (4) TimeCrunch algorithm Reading: (1) VOG: Summarizing and Understanding Large Graphs [pdf] (2) SlashBurn: Graph Compression and Mining beyond Caveman Communities [pdf] (3) Latent Network Summarization: Bridging Network Embedding and Summarization [pdf]  Jianzhu Ma 
02/18  Adversarial Attacks and Defenses  (1) Fast Gradient Sign Method (2) Jacobianbased Saliency Map Attack (3) DeepFool (4) Universal Adversarial Perturbations Optional reading: 1. The Limitations of Deep Learning in Adversarial Settings [pdf] 2. Why deeplearning AIs are so easy to fool [nature article]  Jianzhu Ma 
02/20  Graphical Models I  (1) General concepts (2) Exact inference (3) Sumproduct algorithm (4) Maxproduct algorithm (5) Conditional Random Fields Optional textbooks: (1) “Probabilistic Graphical Models” by By Daphne Koller and Nir Friedman (2) “Graphical Models, Exponential Families, and Variational Inference” by Martin J. Wainwright and Michael I. Jordan (3) Chapter 8 of “Pattern Recognition and Machine Learning” by Christopher M. Bishop  Jianzhu Ma 
02/25  Graphical Models II  (1) Structure learning (2) Gaussian Graphical Model (3) Pseudolikelihood approximation (4) Protein contact prediction (5) DeepLearningbased structure learning Optional reading: (1) Sparse Inverse Covariance Estimation with the Graphical Lasso [pdf] (2) HighDimensional Graphs and Variable Selection with the Lasso [pdf] (3) Protein 3D Structure Computed from Evolutionary Sequence Variation [pdf] (4) AlphaFold: Using AI for scientific discovery [BLOG POST] (5) Foldit computer game [link]  Jianzhu Ma 
02/27  Largescale Graphs  Reading: (1) Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud [pdf] (2) GraphLab: A New Framework For Parallel Machine Learning [pdf] (3) GraphChi: LargeScale Graph Computation on Just a PC [pdf] (4) Software demo [download]  Rajat Verma, Jiqian Dong 
03/03  Network Embedding I  Reading: (1) DeepWalk: Online Learning of Social Representations [pdf] (2) Don't Walk, Skip! Online Learning of Multiscale Network Embeddings [pdf] (3) LINE: Largescale Information Network Embedding [pdf] Optional reading: Efficient Estimation of Word Representations in Vector Space [pdf]  Juan Shu, Qian Zhang, Jiajun Liang 
03/05  Network Embedding II  Reading: (1) node2vec: Scalable Feature Learning for Networks [pdf] (2) struc2vec: Learning Node Representations from Structural Identity [pdf] (3) Inductive Representation Learning on Large Graphs [pdf] Optional reading: Learning Rolebased Graph Embeddings [pdf]  Vinith Budde, Susheel Suresh 
03/10  Learning on Knowledge Graphs  Reading: (1) TransE paper: Translating Embeddings for Modeling Multirelational Data [pdf] (2) TransH paper: Knowledge Graph Embedding by Translating on Hyperplanes [pdf] (3) TransR paper: Learning Entity and Relation Embeddings for Knowledge Graph Completion [pdf] Optional reading: Mining Knowledge Graphs from Text [Tutorial]  Xiaonan Jing, Abida Sanjana, Amira Mamoun 
03/12  Deep Learning on Graphs  (1) General concepts (2) The graph neural network model (3) Popular datasets (4) Gated Graph Sequence Neural Networks Optional reading: (1) The graph neural network model [pdf] (2) Gated Graph Sequence Neural Networks [https:arxiv.orgpdf1511.05493.pdf [pdf]  Jianzhu Ma 
03/24  Deep Reinforcement Learning  (1) Basic reinforcement learning (2) Applications (3) Qlearning (4) Policy Gradient (5) ActorCritic Algorithm Optional reading: (1) Yuyi Li: Deep reinforcement learning [pdf]  Jianzhu Ma 
03/26  Graph Convolutional Networks I  Reading: (1) Spectral networks and locally connected networks on graphs [pdf] (2) Deep Convolutional Networks on GraphStructured Data [pdf]  Matthew Muhoberac, Adam Johnston, Connor Beveridge 
03/31  Graph Convolutional Networks II  Reading: (1) SemiSupervised Classification with Graph Convolutional Networks [pdf] (2) Modeling Relational Data with Graph Convolutional Networks [pdf] (3) Column Networks for Collective Classification [pdf]  Jiang Nan, Pang Qiyuan, Liang Senwei, Xue Jiawei 
04/02  Generative Models of Graphs  Reading: (1) Learning Deep Generative Models of Graphs [pdf] (2) GraphRNN: Generating Realistic Graphs with Deep Autoregressive Models [pdf] (3) Junction tree variational autoencoder for molecular graph generation [pdf]  Omar Eldaghar, Evzenie Coupkova 
04/07  Adversarial Attack on Graphs  Reading: (1) Adversarial Attacks on Neural Networks for Graph Data [pdf] (2) Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications [pdf] Optional reading: (1) Adversarial Attack on Graph Structured Data [pdf] (2) Adversarial Attacks on Node Embeddings via Graph Poisoning [pdf] (3) Attacking Graph Convolutional Networks via Rewiring [pdf]  Jungeum Kim, Jiacheng Li 
04/09  Higherorder Networks  Reading: (1) Structural deep embedding for hypernetworks [pdf] (2) Hyper2vec: Biased random walk for hypernetwork embedding [pdf] (3) HyperSAGNN: a selfattention based graph neural network for hypergraphs [pdf]  Xiao Wang, Maria Pacheco, Sean T Flannery 
04/14  Deep Reinforcement Learning on Graphs I  Reading: (1) NerveNet: Learning Structured Policy with Graph Neural Networks [pdf] (2) Playing TextAdventure Games with GraphBased Deep Reinforcement Learning [pdf]  Zhi Huang, Xiaoyu Xiang 
04/16  Deep Reinforcement Learning on Graphs II  Reading: (1) Graph Convolutional Policy Network for GoalDirected Molecular Graph Generation [pdf] (2) MolGAN: An implicit generative model for small molecular graphs [pdf] (3) Learning Multimodal GraphtoGraph Translation for Molecular Optimization [pdf]  Kendal Graham Norman, Arvind Sundaram 
04/21  Project presentations  TBD  Students 
04/23  Project presentations  TBD  Students 
04/28  Project presentations  TBD  Students 
04/30  Project presentations  TBD  Students 
