CS224W Analysis of Networks

Mining and Learning with Graphs

Projects 2018

Uncovering Political Promotion in China: A Network Analysis of Patronage Relationship in Autocracy
Efficient Simulation of IBD Spectra in Inbred Populations using Network Convolution
A Network Approach to Detect Heavily Affected Cities and Regions using Facebook Movement Data
Link Prediction in Foursquare Social Network
Mapping Hong Kong-Philippine Domestic Employment Networks
A Robustness Study of the Railway System in China
Analysis of Elegans Worm Neural Network
Link Prediction between YouTube Videos using Node Features and Role Attributes
Bundle Generation and Group Recommendation applied to the Steam Video Game Platform
Stanford Memes Group: Network Construction, Community Detection, and Link Prediction
Improving Recall and Precision in Graph Convolutional Networks for Node Classification using Node2Vec Embeddings
Leveraging Network Structures to Reveal Obfuscated and Hidden Attributes in Google+ Networks
vec2rec: Network Embedding for Item-to-Item Recommendation
Exploring the Impact of Black-Box Adversarial Behavior in Graph-based Recommenders
Embeddings for Signed Weighted and Temporal Networks
Empirical Study and Experiments on Information Virality using Twitter Higgs Dataset
Network Analysis and Community detection on GitHub
Quasirandomness and Sidorenko's Conjecture in Directed Networks
Analysis and Prediction of Ride-Sharing and Public Transportation Traffic
Characterizing and Detecting Quarantined Subreddits
Predicting Success of Restaurants on Yelp using Attribute-Specific Spatial Clusters
Personalized Product Recommendation using Customer Expertise
Finding Butterfly Species Pattern: a Case Study on Butterly Similarity Networks
On Representation Power of Character Network Feature Extraction and Inferences
Drug Recommendation System to Minimize Polypharmacy Side Effects
Understanding the Evolution of Divisiveness in the United States Congressional Voting Record through Spectral Methods
Uncovering Modular Structure Underlying Gated Information Transfer in the Mouse Premotor Cortex
Predicting Drug Disease Associations
Needle in the Hay Stack -- Finding Fraud Rings in Transaction Networks
Analyzing the Flow of Currency and Price Movements in the Bitcoin Network
The Impact of Sexual Education Policies on Disease Transmission in Sexual Networks
N-Gram Graphs for Topic Extraction in Educational Forums
Finding Foundations: Using Citation Network Analysis to Trace the Lineages of Academic Knowledge
Network Robustness in the US Airports Infrastructure
An Analysis of the San Francisco Bay Area Public Transit Network
Evolving Community Structures in a Geographic Commuting Graph
Venture Capital Investment Networks: Creation and Analysis
Formulating a New Chemical Reaction Network for Studying Organic Synthesis Pathways
Application of Node2vec: Predict Optimized Treatment for Depression
Predicting Subreddit Toxicity Using Network Properties
Efficient Traffic Forecasting with Graph Embedding
A Graph Based Analysis of Tumor Vessel Networks
Graphical Analysis of the Wordnet Lexicon
Applying Link Prediction to Amazon Product Recommendation
Graph Analysis of Major League Soccer Networks
Directed Bipartite Networks for Mapping Single Cells across Datasets
Embedding-Based Bias Reduction Approach to Movie Review Data
Community Detection and Evolution in Temporal Networks
Via: A Prerequisite Projection Graph for Course Sequence Discovery
Reducing Influenza Propagation Through Air Travel Pathways
Money Moves the Pen: Link Prediction in Congress Bill Co-Sponsorship Networks Using Political Donor Network Information
Predicting the Star Rating of a Business on Yelp using Graph Convolutional Neural Networks
Use of Network Analysis to Model the Effect of HIV PrEP on the Spread of HIV and Gonorrhea
Popularity Growth Analysis and Prediction on Yelp
Characterizing the Urban Form with Persistence-Based Clustering on Graphs
Analyzing and Mitigating Phishing Outbreaks: a Case Study on the 2016 DNC Email Network
Resilient Agriculture: Examining the Robustness of Trade Networks
Temporal Motif Degree Vectors: Efficient Mesoscale Characterization of Temporal Graphs
A Framework for Denoising fMRI Data
Learn Query Similarity Through Link Analysis of a Web-scale Click Graph with an Entity Ontology
Characterizing and Predicting the Economic Network of Corporations
Link Prediction with Enclosing Subgraph
Encoding Visual Information using Node Embeddings
Supervised Community Detection in YouTube Video Network
Analysis of Chinese Venture Capital Networks
A Network Analysis of the Litecoin Blockchain
Unsupervised Document Clustering by Authorial Style through Network-Based Semantic and Syntactic Features
Analyzing Political Relationship Structure in the U.S. Congress
Learning Hyperbolic Representations in Real-World Networks
Detection and Characterization of Virtual Communities in /r/Politics
Using Bayesian Structure Learning and Network Deconvolution to Uncover Direct Effects in Simulated Contagions
Predicting News Source Bias Through Link Structure
Developer Collaboration Prediction on GitHub
Subrecommendit: Recommendation Systems on a Large-Scale Bipartite Graph
Link Prediction and Hub Detection: Analysis of the YouTube Graph Network
Movie Recommendations Based on Character Networks
Competitive Networks for Individual Sports
Palatable Computation: Recipe Generation Using Graph Embeddings
Node Representation and Link Prediction in Multi-Edge Networks
Graph-Based Recommendations of Amazon Products
Identifying Trends and Investigating Predictive Power in the Global Conflict Network
Application of a Customized PageRank Algorithm to Evaluate the Influence of Species and Reactions in a Hydrocarbon Reaction Network
Analyzing Political Communities on Reddit
Signed Weighted Graph Community Detection for Spatial Correlation in Earthquake Intensity Measurement Networks
Community Detection for the Twittersphere during the Kavanaugh Confirmation Hearings
Understanding Systemic Risk in Global Sovereign Credit Markets: a Network Topology Approach to Time-Series Analysis with Financial Applications
Node Classification in Social Networks Using Semi-Supervised Learning
Weighted Signed Network Embeddings
Learning to Generate Industrial SAT Instances
Improving New Editor Retention on Wikipedia
Microloan Mania: Community Detection in Kiva
Network Analysis of Chord Progressions in Rock and Jazz Music
Exploring the Functional Networks of the Resting Brain with Topological Data Analysis
Network Analysis of Coordinated Iranian Tweets
A Visual Exploration of the Political Landscape of Reddit
Recommendation Systems for the Netflix Network
Temporal Analysis of International Relations Networks
Link Sign Prediction in Signed Networks
Fraud Detection in Signed Bitcoin Trading Platform Networks
Predicting Fake News: Analyzing the Reference Network Structure of News Articles
Classifying ADHD from Resting State fMRI

Project Ideas

Temporal Walk Based Centrality Metric for Graph Streams

In dynamic networks, characterizing the temporal centrality of a node is a challenging task. In this work we develop an online updateable temporal network centrality measure. The metric is based on the concept of time-respecting walks containing a sequence of adjacent edges with timestamps ordered in time. We compare our algorithm against static centrality metrics like PageRank on data sets containing a stream of edges.

Prerequisites: Python
Contact: Robert Palovics

Empirical Comparison of Euclidean and Hyperbolic Network Embeddings

The project aims to conduct a large empirical evaluation of Euclidean and hyperbolic network embeddings. Euclidean embeddings, such as Node2vec, DeepWalk, and various GCN methods, have become a popular approach for learning with graphs and have proven successful in numerous important applications. When graphs have some latent hierarchical structure they might be more accurately embedded not in Euclidean but in hyperbolic space. Students will use networks from SNAP and BioSNAP, compute Euclidean and hyperbolic embeddings, and compare both types of embeddings for several prediction tasks, including node classification, link prediction, and community detection.

Prerequisites: Python; SNAP.py; PyTorch, TensorFlow or other deep learning framework
Contact: Marinka Zitnik

Network science of human tissues

All cells in the human body have basically the same DNA and the same set of genes, however, cells organize themselves in tissues, such as lung and brain, which are very different from each other, even to the naked eye. How can we explain these differences? The project will use tissue-specific gene interaction networks from BioSNAP and perform a network science study comparing and analyzing gene interaction networks from many different human tissues.

Prerequisites: Python, SNAP or SNAP.py, PyTorch or TensorFlow, any introductory bioinformatics course
Contact: Marinka Zitnik

BioSNAP: Stanford Biomedical Network Dataset Collection

We are building BioSNAP, Stanford Biomedical Network Dataset Collection (http://snap.stanford.edu/biodata). The goal of this project is to improve BioSNAP in several different directions. We would like to construct additional networks, analyze them, and incorporate them into BioSNAP. We would also like to improve the utility of BioSNAP by including mappings, which would allow us to easily construct large heterogeneous graphs.

Prerequisites: SNAP or SNAP.py, any introductory bioinformatics course
Contact: Marinka Zitnik

Weighted signed network embeddings for link prediction

Signed social networks represent positive and negative relationships (e.g., trust/distrust and like/dislike) between nodes. These relations can be weighted to represent the strength of the sentiment. In this project, you will develop embedding based techniques to predict links and link weights in signed social networks. We will use it on bitcoin trust networks and in-person deception networks.

Contact: Srijan Kumar

Weighted signed network embedding for fraud detection

Signed social networks represent positive and negative relationships (e.g., trust/distrust and like/dislike) between nodes. These relations can be weighted to represent the strength of the sentiment. In this project, you will develop embedding based techniques to identify fraudulent users in weighted signed networks, with applications to identify scammers in bitcoin networks and e-commerce websites.

Contact: Srijan Kumar

Signed temporal network embedding

Signed social networks represent positive and negative relationships (e.g., trust/distrust and like/dislike) between nodes. These relations can be weighted to represent the strength of the sentiment. These networks can evolve over time. In this project, you will develop graph embedding based algorithms to predict future relations.

Contact: Srijan Kumar

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