Agent-based models capture how local decisions give rise to collective phenomena, but are rarely learnable from real data. This study introduces a differentiable framework that reconstructs individual behavioral rules while preserving interaction structure and stochasticity through the integration of graph neural networks and diffusion models. The approach bridges mechanistic modeling and machine learning, enabling empirical testing of theories about decentralized and emergent social and ecological systems.

Agent-based models of opinion formation often rely on repeated simulations to approximate observed collective outcomes, limiting both interpretability and efficiency. This work introduces a likelihood-based estimation approach that directly connects model parameters to data through probabilistic generative modeling, allowing opinions and interactions to be inferred from evidence rather than tuned by trial. By applying it to the bounded-confidence model, the study advances a data-driven understanding of how opinions evolve through social influence.

Moderation policies aim to reduce harm online, yet banning conspiratorial communities can trigger migration and reformation elsewhere. Comparing users of banned Reddit groups with their counterparts on Voat, this study shows that conspiracy networks reconstruct their social ties and activity levels after deplatforming, sustaining both engagement and toxicity. The results underscore the adaptive resilience of conspiratorial ecosystems, calling for moderation strategies that account for cross-platform behavior.

Online debates are often portrayed as ideological echo chambers, yet the structure of conversation can mirror offline social boundaries instead. Analyzing millions of interactions in Reddit’s news discussions, this study shows that users connect more across ideological lines than across demographic divides such as age and income. The findings reveal how latent social stratification shapes digital communication, suggesting that online polarization often reproduces material divisions rather than belief systems.

Online political engagement depends on how users shift attention across communities. This paper proposes an attention-flow graph that captures user migration between subreddits, identifying gateways and bridges that connect mainstream, conspiratorial, and extremist spaces. The analysis shows how conspiracy forums can act as intermediaries in radicalization pathways, mapping the structural channels through which political audiences reorganize.

The idea that social media create echo chambers overlooks how users actually interact across partisan lines. Studying millions of comments around the 2016 U.S. elections, this analysis finds that cross-cutting exchanges between Trump and Clinton supporters were more frequent than within-group ones, though asymmetrical and demographically patterned. The findings challenge the dominant polarization narrative, showing how political interaction networks can sustain contact even amid ideological division.

Agent-based models explain how opinions evolve through interaction, yet they often remain detached from real data. This work develops a probabilistic inference framework that learns latent opinions and model parameters directly from social traces, retaining the interpretability of theoretical models while gaining empirical grounding. Applied to political conversations on Reddit, it provides a data-driven test of opinion dynamics theories, revealing limited evidence for the backfire effect in online debate.

Simulating how ideas spread online helps test hypotheses about social contagion and influence. This study introduces a model that generates synthetic information cascades by combining users’ topical interests, virality, and community pressure, producing propagation patterns that mirror real social media data. The framework enables controlled experimentation on how interest alignment and network structure jointly shape the diffusion of information.

Understanding how attributes interact within networks is key to modeling complexity in social and informational systems. This work extends the MGJ model to estimate latent feature–feature interactions that drive or inhibit link formation in large graphs. By reformulating the problem as a perceptron-like learning task, it introduces scalable algorithms that infer structural regularities from high-dimensional relational data, bridging network analysis and machine learning foundations later applied to socio-technical behavior.

Collaborative knowledge systems evolve through messy, user-generated hierarchies. This study proposes a centrality-based pruning method that cleanses the Wikipedia category network by identifying structural redundancies and inconsistencies. By relying solely on endogenous information, it demonstrates how collective curation and algorithmic structure can be combined to improve the organization of open knowledge — work that later became the basis for a Wikipedia-based benchmark for graph neural networks, now widely used in research.

Discovering non-obvious relations in knowledge systems requires models that go beyond surface similarity. This paper presents an online margin-based learning algorithm that infers a latent matrix of category interactions to uncover hidden connections within Wikipedia’s hyperlink structure. The method efficiently scales to large graphs, revealing how semantic structures and user-generated organization jointly shape information discovery.

Networks emerge from both shared attributes and competition among their participants. This work introduces a latent-attribute generative model where connections depend on feature overlap and a node’s fitness to transmit attributes, allowing even new nodes to compete for links. The model reproduces key empirical regularities of real systems and provides a parsimonious explanation of growth and inequality in complex networks.

Recommender systems usually rely on behavioral similarity, but trust and delegation also structure how people share taste. This project introduces Liquid FM, a music recommender based on viscous democracy, where users vote for experts whose preferences propagate through the network. By turning recommendation into a collective decision process, it experiments with new forms of democratic influence and accountability in algorithmic systems.