@inproceedings{10.1145/3615896.3628345,
author = {Gallagher, Ketevan and Anderson, Taylor and Crooks, Andrew and Z\"{u}fle, Andreas},
title = {Synthetic Geosocial Network Generation},
year = {2023},
isbn = {9798400703584},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3615896.3628345},
doi = {10.1145/3615896.3628345},
abstract = {Generating synthetic social networks is an important task for many problems that study humans, their behavior, and their interactions. Geosocial networks enrich social networks with location information. Commonly used models to generate synthetic social networks include the classical Erd\H{o}s-R\'{e}nyi, Barab\'{a}si-Albert, and Watts-Strogatz models. However, these classic social network models do not consider the location of individuals. Real-world geosocial networks do exhibit a strong spatial autocorrelation, thus having a higher likelihood of a social connection between agents that are spatially close. As such, recent variants of the three classical models have been proposed to consider location information. Yet, these existing solutions assume that individuals are located on a uniform lattice and exhibit certain limitations when applied to real-world data that exhibits clusters. In this work, we discuss these limitations and propose new approaches to extend the three classic social network generation models to geosocial networks. Our experiments show that our generated synthetic geosocial networks address the shortcomings of the state-of-the-art models and generate realistic geosocial networks that exhibit high similarity to real-world geosocial networks.},
booktitle = {Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising},
pages = {15–24},
numpages = {10},
keywords = {geosocial networks, network generation, synthetic social networks, erdos-renyi, watts-strogatz, barabasi-albert},
location = {Hamburg, Germany},
series = {LocalRec '23}
}

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abstractNote = {NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.},
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@software{joris_van_den_bossche_2024_12625316,
  author       = {Joris Van den Bossche and
                  Kelsey Jordahl and
                  Martin Fleischmann and
                  Matt Richards and
                  James McBride and
                  Jacob Wasserman and
                  Adrian Garcia Badaracco and
                  Alan D. Snow and
                  Brendan Ward and
                  Jeff Tratner and
                  Jeffrey Gerard and
                  Matthew Perry and
                  cjqf and
                  Geir Arne Hjelle and
                  Mike Taves and
                  Ewout ter Hoeven and
                  Micah Cochran and
                  Ray Bell and
                  rraymondgh and
                  Matt Bartos and
                  Pieter Roggemans and
                  Lucas Culbertson and
                  Giacomo Caria and
                  Nicholas YS Tan and
                  Nick Eubank and
                  sangarshanan and
                  John Flavin and
                  Sergio Rey and
                  James Gardiner},
  title        = {geopandas/geopandas: v1.0.1},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v1.0.1},
  doi          = {10.5281/zenodo.12625316},
  url          = {https://doi.org/10.5281/zenodo.12625316}
}

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}

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}