- Generate large random networks using Erdős–Rényi (ER) model and Preferential Attachment (PA) model.
- Analyze community structure of large networks, including Giant Connected Component (GCC) size and modularity.
- Modify preferential attachment model to penalize the age of a node in large random graph.
- Implemente random walk on large random network to simulate Google’s PageRank algorithm.
- Explore structural properties of undirected social network (Facebook dataset) using connectivity and degree distribution.
- Explore structural properties of personalized network of core nodes using Fast-Greeedy, Edge-Betweenness, and Infomap community detection algorithms.
- Explore characteristics of nodes in the personalized networks using Embeddedness and Dispersion.
- Implement friend recommendation in personalized networks using neighborhood-based measures, including common neighbor measure, Jaccard measure, and Adamic-Adar measure, and evaluate with average accuracy measure.
- Explore the community structure of directed social network (Google+ dataset), defined by homogeneity and completeness.
- Model the environment of the agent by Markov Decision Process (MDP).
- Implement value iteration algorithm of Reinforcement Learning (RL).
- Implement the IRL algorithm using Linear Programming (LP) formulation.
- Evaluate the performance of IRL algorithm by comparing the extracted reward function and optimal policy map with ground truth.
- Construct a correlation graph using correlation coefficient computed among stock-return time series data.
- Extract the Minimum Spanning Tree (MST) of the correlation graph and interpret it.
- Predict the market sector of an unknown stock, and evaluate the performance using sector clustering in MST.
- Simulate the influence of traffic block on the flow between certain places, and estimate how many roads could be blocked before paralyzing the traffic in Los Angeles area.