Location-Based Social Networks (LBSNs), with the most popular being Yelp and Foursquare, have become a vital part of our society due to their assistance on users needs. For example, foreign tourists in San Fransisco can easily find highly-reputed restaurants even if it is their first time in the city. As LBSNs have accumulated various information about users and businesses within it, this information allows us to solve practical problems of predicting the survival rate of the businesses in LBSNs.
As an extension of the survival rate prediction, can we accurately predict the popularity of the business within LBSNs based on a few initial information on the business? One can use various techniques such as traditional ML algorithms (e.g., Logistic Regression or Random Forest) or advanced DL methods (e.g., Transformers or Graph Neural Networks) or develop a novel DL method. Finally, this work can help new business owners in LBSNs to make their business more prosperous.
Reference papers (can be a good starting point)
 2018. IMWUT. The Role of Urban Mobility in Retail Business Survival. K DSilva et al.
 2017. WWW. Restaurant Survival Analysis with Heterogeneous Information. J Lian et al.
1. You will need to be self-motivated and proactive, which means you should be able to set your own milestones and finish them on time.
2. Strong interest in analyzing data and finding common or new patterns from it
3. Programming knowledge (Python, R) is a plus
4. Data, network analytics tools (SNAP, Gephi, NetworkX) is a plus
Web development skills (HTML, CSS, JS) is a plus.
1. Opportunity to gain hands-on experience in Social Network Analysis and Machine Learning research
2. Opportunity to tackle a challenging and novel problem that no one has solved before
3. Opportunity for having a top-tier academic publication, depending on the quality and the amount of novel contribution of the project