Volume 34, Number 2 (2025)

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Grid-World Modeling of Area-Population Dynamics Based on Data for Indian Cities
Dvarkesh Ghatol, Muhammad Alfas, Akshay Mattoo and Shaurya Shriyam

Among the major applications of network science, significant attention has been paid to modeling smart cities and mobility. Modeling cities and urban systems is also important from the perspective of policy-making toward sustainable development. By representing cities or areas in a city as nodes and the population flow among them using edges, we try to build network models that capture the essence of urban systems and growth patterns. Our simulations are based on predefined parameters and specific rules to govern area and population growth. We primarily model area expansion and population growth dynamics using two grid-based models. The first model uses real-world data, whereas the second model is inspired by cellular automata and implemented in two versions: random and contiguous. We also computed complexity metrics based on approximate entropy and found that the complexity values associated with area-population growth dynamics were always higher in the random cases as compared to their contiguous counterparts. We have also shown validation results and shared interesting insights based on our simulation runs.

Keywords: urban informatics; simulation modeling; cellular automata; complexity estimation

Cite this publication as:
D. Ghatol, M. Alfas, A. Mattoo and S. Shriyam, “Grid-World Modeling of Area-Population Dynamics Based on Data for Indian Cities,” Complex Systems, 34(2), 2025 pp. 161–201.
https://doi.org/10.25088/ComplexSystems.34.2.161

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Community Discovery on Dynamic Graphs with Edge-Local Differential Privacy
Sudipta Paul, Fatemeh Sadjadi, Vicenç Torra and Julián Salas

Interactions among different elements of complex networks are organized in a structured manner. The collective behavior of the elements of these networks is organized according to community structure. Several methods have been defined to automatically detect these substructures in the field known as community discovery. Most of the methods have been applied to static or aggregated data. Recently the identification of evolving communities has gained more attention. Studying the relations among individuals yields insights on how communities form and evolve, but there are some limits that should be enforced to respect individuals’ privacy while sharing and collecting their data. Privacy-protection techniques have been commonly applied to static data, while there are few methods that work on dynamic data. Recently, there have been some approaches to protect dynamic graphs with local edge-differential privacy that have been tested for community discovery applications. However, the evolution of the communities over time has not been evaluated on the privacy-protected data. We test the utility considering community discovery and evolution in time-varying networks for such local-edge-ε-differential privacy methods. We show empirically how these algorithms can provide privacy while preserving the community life cycles, for their privacy-aware study.

Keywords: edge local differential privacy; dynamic graphs; community discovery

Cite this publication as:
S. Paul, F. Sadjadi, V. Torra and J. Salas, “Community Discovery on Dynamic Graphs with Edge-Local Differential Privacy,” Complex Systems, 34(2), 2025 pp. 203–215.
https://doi.org/10.25088/ComplexSystems.34.2.203

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Toward Human Mobility Pattern Detection through Sparse Data
Daniel Maksimov, Pillot Benjamin and Gervet Carmen

Social media data is an efficient means to understand human mobility through the spatial and temporal patterns of the users. Those patterns can help us discover and define mobility communities, which we specify as a group of users sharing the same spatiotemporal patterns. In this paper, we focus on a particular social media platform, X, formerly known as Twitter. X features geolocalized posts, also known as geolocalized tweets, that can be gathered through the platform’s API. Our goal is to gather and analyze geolocalized tweets from two different cities, namely Brasilia and London, over a one-year period, to extract common spatiotemporal patterns among users and carry out a comparative analysis between cities.
         Social media data can be labeled as sparse data relative to other datasets. However, such data also offers the benefits of: (1) not being influenced as an object of a foreseen or tailored study; and (2) providing naturally both a textual contextualization and GPS geolocalization. Our goal is to exploit such assets toward human mobility pattern detections. In this paper, we show how two different methods, namely K-means clustering based on a user’s vectorized text and the Louvain community detection algorithm on a user graph based on similarity score, can be used successfully to extract mobility patterns. We provide two case studies using London and Brasilia data for the year 2022 and carry out a comparative analysis of our methodology, highlighting common pattern behaviors.

Keywords: human mobility; deep learning; Twitter; X; spatiotemporal pattern

Cite this publication as:
D. Maksimov, P. Benjamin and G. Carmen, “Toward Human Mobility Pattern Detection through Sparse Data,” Complex Systems, 34(2), 2025 pp. 217–233.
https://doi.org/10.25088/ComplexSystems.34.2.217

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Musical Composition and Two-Dimensional Cellular Automata Based on Music Intervals
Igor Lugo and Martha G. Alatriste-Contreras

This paper uses a theoretical approach to explore the applicability of a two-dimensional cellular automaton based on melodic and harmonic intervals in random arrays of musical notes. Our aim is to explore alternative uses for a cellular automaton in the musical context for better understanding musical creativity. We use the complex systems and humanities approaches as a framework for capturing the essence of creating music based on rules of music theory. Findings suggest that such rules matter for generating large-scale patterns of organized notes. Therefore, our formulation provides a novel approach for understanding and replicating aspects of musical creativity.

Keywords: musical composition; music creativity; music intervals; complex systems; cellular automata; data analysis

Cite this publication as:
I. Lugo and M. G. Alatriste-Contreras, “Musical Composition and Two-Dimensional Cellular Automata Based on Music Intervals,” Complex Systems, 34(2), 2025 pp. 235–251.
https://doi.org/10.25088/ComplexSystems.34.2.235

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Cooperation as Well as Learning: A Commentary on “How Learning Can Guide Evolution”
Conor Houghton

According to the Baldwin effect, learning can guide evolution. This does not suppose that information about what has been learned is transferred back into the genetic code: in the Baldwin effect, complex multi-gene characteristics are discovered through learning but acquired through standard selectionist evolutionary processes. Learning serves to improve the search by giving value to a partial, and otherwise useless, subset of the required genes. An elegant and concrete treatment of the Baldwin effect is given in “How Learning Can Guide Evolution,” a 1987 paper by G. E. Hinton and S. J. Nowlan [1]. This includes a simple but revealing simulation illustrating the effect. As a commentary on that paper, a similar simulation is used here to demonstrate that cooperation can also guide evolution. Like learning, cooperation has a clear benefit to survival, but what is proposed here is a small addition: that cooperation, like learning in the Baldwin effect, can also allow complex characteristics to be discovered and acquired much faster than they otherwise would. This suggests an additional benefit of social behavior and suggests that social animals have a broader evolutionary path toward some complex adaptations.

Keywords: evolution; Baldwin effect; cooperation

Cite this publication as:
C. Houghton, “Cooperation as Well as Learning: A Commentary on “How Learning Can Guide Evolution”,” Complex Systems, 34(2), 2025 pp. 253–258.
https://doi.org/10.25088/ComplexSystems.34.2.253