Publications

2024

1. Mechanical prions: Self-assembling microstructures

   Mathieu Ouellet, Dani S. Bassett, Lee C. Bassett, Kieran A. Murphy, and Shubhankar P. Patankar

   arXiv, 2024

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   Prions are misfolded proteins that transmit their structural arrangement to neighboring proteins. In biological systems, prion dynamics can produce a variety of complex functional outcomes. Yet, an understanding of prionic causes has been hampered by the fact that few computational models exist that allow for experimental design, hypothesis testing, and control. Here, we identify essential prionic properties and present a biologically inspired model of prions using simple mechanical structures capable of undergoing complex conformational change. We demonstrate the utility of our approach by designing a prototypical mechanical prion and validating its properties experimentally. Our work provides a design framework for harnessing and manipulating prionic properties in natural and artificial systems.

2023

1. Architectural styles of curiosity in global Wikipedia mobile app readership

   Dale Zhou, Shubhankar P. Patankar, David M. Lydon-Staley, Perry Zurn, Martin Gerlach, and Danielle S. Bassett

   PsyArXiv, Nov 2023

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   Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we expand an analysis of a laboratory study with 149 participants browsing Wikipedia to 482,760 readers using Wikipedia’s mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we provide the first replication of two distinctive architectural styles of curiosity: that of the busybody and of the hunter. Further, we find that latent dimensions of browsing patterns explain the variation in knowledge network structure, and reflect the existence of a third architectural style of curiosity—the dancer—which was previously predicted by a historico-philosophical examination of texts written across the last 2 millennia and is characterized by creative modes of knowledge production. Finally, across languages and countries, we identify novel associations between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. Collectively, these results advance our understanding of Wikipedia’s readership globally and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

2. Curiosity as filling, compressing, and reconfiguring knowledge networks

   Shubhankar P. Patankar, Dale Zhou, Christopher W. Lynn, Jason Z. Kim, Mathieu Ouellet, Harang Ju, Perry Zurn, David M. Lydon-Staley, and Dani S. Bassett

   Collective Intelligence, Oct 2023

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   Theoretical constructs, such as the information gap theory and compression progress theory, seek to explain how humans practice curiosity. According to the former, curiosity is the drive to acquire information missing from our understanding of the world. According to the latter, curiosity is the drive to construct parsimonious mental world models. To complement the densification and simplification processes inherent to these frameworks, we propose the conformational change theory, wherein we posit that curiosity builds mental models with marked conceptual flexibility. We formalize curiosity as a knowledge-network-building process to investigate each theoretical account for individuals and collectives. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We find that curiosity fills gaps and constructs increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each account is explanatory, clarifying their complementary and distinct contributions. Our findings offer a novel networks-based perspective that harmonizes with (and compels an expansion of) the traditional taxonomy of curiosity.

3. Intrinsically motivated graph exploration using network theories of human curiosity

   Shubhankar P. Patankar, Mathieu Ouellet, Juan Cervino, Alejandro Ribeiro, Kieran A. Murphy, and Danielle Bassett

   In Proceedings of the Second Learning on Graphs Conference, Nov 2023

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   Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosity: the information gap theory and the compression progress theory. The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment. We use these proposed features as rewards for graph neural-network-based reinforcement learning. On multiple classes of synthetically generated graphs, we find that trained agents generalize to longer exploratory walks and larger environments than are seen during training. Our method computes more efficiently than the greedy evaluation of the relevant topological properties. The proposed intrinsic motivations bear particular relevance for recommender systems. We demonstrate that next-node recommendations considering curiosity are more predictive of human choices than PageRank centrality in several real-world graph environments.

2020

1. Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks

   Shubhankar P. Patankar, Jason Z. Kim, Fabio Pasqualetti, and Danielle S. Bassett

   Network Neuroscience, Nov 2020

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   The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain’s large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain’s diverse mesoscale structure supports transient communication dynamics.