Analysis and Visualization of Spatial-Social Networks

Computer Science Department

Kent State University

Project Introduction

In this project, we tackle a useful problem, keyword-based community search over spatial-social networks (KCS-SSN), which retrieves a community of users who are constrained by query keywords (w.r.t. user interests/preferences and POI keywords) and have high social and spatial cohesiveness (e.g., constraints of (k, d)-truss and average road-network distances). We propose efficient and effective algorithms to enable fast retrieval of community answers and design a user-friendly graphical user interface (GUI) to interact with users.

Contributions

● Formalized the problem of community search over spatial-social networks

● Provide a user-friendly interface to plot, visualize, and analyze spatial-social network

● Design efficient techniques for keyword-based community searching

Documentation

COF 2021 Presentation: Efficient and Effective Management and Analytics Over Spatial-Social Networks

URLs: http://www.cs.kent.edu/~xlian/projects/COF2022_CS_SSN/Efficient and Effective Management and Analytics Over Spatial-Social Networks.pptx

https://docs.google.com/presentation/d/1_H94K5i0Hx6vJhQ__pi_R6ELPO8fiaVeH6fShm1R1nI/edit?usp=sharing

COF 2022 Poster Presentation: COF2022_Poster.pdf

URL: http://www.cs.kent.edu/~xlian/projects/COF2022_CS_SSN/COF2022_Poster.pdf

https://drive.google.com/file/d/1gf2cTvYFxLJMmSPWHIXGfnkc33O4_DHR/view?usp=sharing

GitHub Code Repository: https://github.com/InfernoX5515/Spatial-SocialNetworks

Demo

When the application is first loaded, the user is shown a summary of the loaded road and social network datasets.

The user can switch views and execute a cluster query on the data, with real-world clusters being displayed on the right with interactive, draggable clusters appearing on the right.

The user can also execute a KD query on the query using the toolbar at the top of the application. Users who satisfy the query are displayed as interactive nodes on the left. User's physical locations are displayed on the right, with the query user being a red star.