Towards Generating Realistic Geosocial Networks
Abed Al Rhman Sarsour
Panagiotis Bouros

Theodoros Chondrogiannis

Geosocial networks• Networks that model both
– User social connections or interactions– Geo-referenced actions• In academia, research on– Modeling– Indexing and query processing– Analysis• Influence maximization, community detection etc.

November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising1Dude, where are my datasets?
• Very few geosocial networks publicly available• Use an official API– Limitations on queries or downloaded data per day– Fees for unlimited access• Use synthetic geosocial networks– Realistic– Potentially large
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7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising2

Background• Social network generation– Power-law vertex-degree distribution– Small diameter– Progressively constructed using preferential attachment• Rich gets richer approach• Spatial data generation– Multidimensional data generation– Variety of distributions in space• Uniform, clustered, diagonal• Geosocial network generation– [Alizadeh et al. 2017] and [Gallagher et al. 2023]• Both spatial and social dimensions evolve at the same time

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7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising3

ContributionsüGeneration of realistic geosocial networks– Mimic the characteristics of real networksüThree types of synthetic networks– Different characteristics– Cover different scenarios and applicationsüGenerator prototype– Modular architecture– Decouple graph generation from spatial data generation– Build upon existing generators– Reuse existing datasets
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7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising4Types of synthetic networks (1/3)• Characteristics– One type of vertices• Users of the network– One type of edges• Relationships between users, LIKE,FRIEND_OF, FOLLOW– Subset of vertices spatial annonated• Workplace, residence etc.

• Example– Academic geosocial network• Co-authorship graph• Coordinates of affiliation
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7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising5

Types of synthetic networks (1/3)• Characteristics– One type of vertices• Users of the network– One type of edges• Relationships between users, LIKE,FRIEND_OF, FOLLOW– Subset of vertices spatial annonated• Workplace, residence etc.

• Example– Academic geosocial network• Co-authorship graph• Coordinates of affiliation
November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising6

Types of synthetic networks (1/3)• Characteristics– One type of vertices• Users of the network– One type of edges• Relationships between users, LIKE,FRIEND_OF, FOLLOW– Subset of vertices spatial annonated• Workplace, residence etc.

• Example– Academic geosocial network• Co-authorship graph• Location of affiliation
November 13, 2023

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Types of synthetic networks (2/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices• Venues, Points of Interest etc.

– Spatial-to-social edges• One-to-many• Example– Foursquare geosocial network• Users CHECK_IN in venues

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7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising8

Types of synthetic networks (2/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices• Venues, Points of Interest etc.

– Spatial-to-social edges• One-to-many• Example– Foursquare geosocial network• Users CHECK_IN in venues

November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising9Types of synthetic networks (2/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices• Venues, Points of Interest etc.

– Spatial-to-social edges• One-to-many• Example– Foursquare geosocial network• Users CHECK_IN in venues

November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising10

Types of synthetic networks (2/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices• Venues, Points of Interest etc.

– Spatial-to-social edges• One-to-many• Example– Foursquare geosocial network• Users CHECK_IN in venues

November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising11

Types of synthetic networks (3/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices– Spatial-to-social edges• One-to-one• Example– GeoTweets geosocial network• Users make geo-annotated posts
November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising12Types of synthetic networks (3/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices– Spatial-to-social edges• One-to-one• Example– GeoTweets geosocial network• Users make geo-annotated posts
November 13, 2023

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Types of synthetic networks (3/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices– Spatial-to-social edges• One-to-one• Example– GeoTweets geosocial network• Users make geo-annotated posts
November 13, 2023

7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising14

Types of synthetic networks (3/3)• Characteristics– Two types of vertices and edges– Social core of vertices and edges• Users of the network• Relationships between users– Spatially annonated vertices– Spatial-to-social edges• One-to-one• Example– GeoTweets geosocial network• Users make geo-annotated posts
November 13, 2023

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Generation process
Generators

Combiners

Graph
Generator

Gs

Social network

Gc
Spatial Data
Generator

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Geospatial data

Geosocialnetwork

Gp

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Prototype
Graph generation––––

Outputs a .gr file
Barabasi albert [Albert and Barabasi 1999]
Scale-free [Bollobás et al. 2003]
Powerlaw cluster [Holme and Kim 2002]

Spatial data generation––––

Outputs a .co file
[Katiyat et al. 2020]
Point or rectanglesUniform, clustered, diagonal space dsitributions

Gs

1st type: Randomly select a subset of vertices from .gr to assign a geometry from .co

Gc

2nd type: Create spatial vertices from .co, connect each to one or more social vertices from .gr

Gp

3rd type: Create spatial vertices from .co, connect each to one social vertex from .gr

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7th ACM SIGSPATIAL International Location-based Recommendations, Geosocial Networks and Geoadvertising

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Future work• Study how realistic the generated networks are– Compare against available real networks• Investigate new types of geosocial network or generation approaches– [Gallagher et al. 2023]• Consider vertex and edge labels• Develop an interactive UI for generation and visualization
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Thank you!

Questions?
https://github.com/pbour/geosocialgenerator
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