Inferring social communities using unobserved future interests

2018 COE Engineering Design Project (EB05)

Faculty Lab Coordinator

Ebrahim Bagheri

Topic Category

Software / Data Engineering


Like-minded user communities in social networks are usually identified by either considering explicit structural connections between users (link analysis) or users' topics of interest expressed in their posted contents (content analysis). The effective identification of user communities on social networks enables applications such as news recommendation, user prediction and community selection, just to name a few.


The objective of this project will be to identify user communities based on the similarity of their future unobserved topics of interest/

Partial Specifications

Students will learn about cutting edge techniques in Natural Language Processing and Information Retrieval.

Suggested Approach

The work will build upon and improve the work presented in

Group Responsibilities

The group will be responsible to replicate the work presented in the baseline paper, work with the FLC to further enhance the algorithm, implement the enhancements, perform experiments and report on the findings. All students will be responsible for understanding the baseline paper and participating in group brainstorming sessions.

Student A Responsibilities

Preparation of the dataset for the experiments and assist with the implementation of the enhancements to the baseline.

Student B Responsibilities

Implementation and Replication of the baseline code.

Student C Responsibilities

Leading the implementation of the enhanced code as well as running the experiments.

Course Co-requisites



EB05: Inferring social communities using unobserved future interests | Ebrahim Bagheri | Not yet submitted at No time