Analysis and Visualization of Spatial-Social Networks

Computer Science Department

Kent State University

Project Introduction

In this project, we tackle a useful problem, Querying Over Spatial Social Networks, that returns communities (i.e., groups of users) with strong influence factors such as keywords, relationships, and POIs satisfying some constraints. Users are combined into clusters based on their degree of similarity with other users. The degree of similarity is then calculated with all other clusters. We propose efficient and effective algorithms to enable fast user clustering, indexing, and querying of users and design a user-friendly graphical user interface (GUI) to interact with our developed prototype system. Specific attention will be placed on enhancing the efficiency of the community search query.

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 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

COF 2023 Poster Presentation:

URL: https://drive.google.com/file/d/1EGClppj0jEmS718Y_hgTz11xyF5ictWC/view?usp=sharing

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

Awards

2021 COF Poster Symposium

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2023 COF Poster Symposium – Award of Excellence for Outstanding Presentation

Demo

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

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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.

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The user can also execute a community search 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.