GGBond Multi-layer Social Network Framework

• The paper introduces a dynamic, agent-based simulation that models multilayer social networks by integrating content, personality, and structural factors via convex combinations.
• The framework features a five-module cognitive architecture that captures memory decay, affective shifts, and motivational decision-making using the ICR² engine.
• The study evaluates recommender interventions and emergent phenomena such as echo chambers, providing insights for improved algorithm design and ethical social simulations.

The GGBond Multi-layer Social Network Framework is a high-fidelity, agent-based simulation environment designed for the paper and evaluation of recommendation algorithms and social influence dynamics within evolving, heterogeneous social systems. By combining a dynamically evolving multilayer social graph with psychologically inspired, cognitively rich AI agents, the framework enables in-depth exploration of long-term effects of recommender interventions, user preference drift, and emergent social phenomena—addressing the limitations of static evaluation with offline datasets (Zhong et al., 27 May 2025).

1. Structural Composition of the GGBond Graph

The foundational data structure of the GGBond framework is a multilayer (heterogeneous) social graph, where each node corresponds to a Sim-User Agent endowed with personalized structural and psychological attributes. Edges between nodes are stratified into distinct semantic layers:

• Interest Graph Layer: Edges encode the overlap in consumed content (e.g., liked movies) between agents u and v, using Jaccard similarity:

$$w^{(\text{int})}_{uv} = \frac{|\mathcal{M}_u \cap \mathcal{M}_v|}{|\mathcal{M}_u \cup \mathcal{M}_v|}$$

• Personality Graph Layer: Connections reflect psychological similarity via cosine distance on the Big-Five profile vectors $\mathbf{b}_u$:

$$w^{(\text{pers})}_{uv} = \cos(\mathbf{b}_u, \mathbf{b}_v) = \frac{\mathbf{b}_u \cdot \mathbf{b}_v}{\|\mathbf{b}_u\| \|\mathbf{b}_v\|}$$

• Structural Homophily Graph Layer: Encodes demographic homophily (e.g., same occupation, age), with weights:

$$w^{(\text{struct})}_{uv} = \mathbb{1}_{\text{same}(t_{u,2}, t_{v,2})} + \mathbb{1}_{\text{same}(t_{u,3}, t_{v,3})}$$

The global unified layer aggregates these via a convex combination: $$w_{uv} = \alpha w_{uv}^{(\text{int})} + \beta w_{uv}^{(\text{pers})} + \gamma w_{uv}^{(\text{struct})}$$, where $\alpha + \beta + \gamma = 1$. The edge weights, and in turn the network topology, are dynamically updated to reflect ongoing agent interactions and evolving states (e.g., personality drift triggered by social influence). This architecture simultaneously models direct content-based similarity, latent psychological affinities, and demographic commonality, with clear extensibility to further edge types and dynamic rewiring mechanisms (Zhong et al., 27 May 2025).

2. Sim-User Agent Cognitive Architecture

Each Sim-User Agent comprises a five-module cognitive stack designed to mirror core psychological processes:

• Module 0: Language Reasoning Core Utilizes a LLM (e.g., DeepSeek-R1) to translate internal state-driven decisions into natural language artifacts—activated post hoc after decision events.
• Module 1: Individual Cognition Sub-divided into (a) Episodic Memory, modeled using exponential temporal decay ($w_t = \exp(-\lambda_\text{mem} \cdot \Delta t)$), (b) Affective State Machine (valence $V$, arousal $A$) evolving per $V_{t+1} = V_t + \sigma_V M$, $A_{t+1} = A_t + \sigma_A|M|$, with $M$ as the individual satisfaction, and (c) Preference Model, which incrementally updates the long-term preference vector $\mathbf{p}_u$ via smoothed averaging with content embeddings.
• Module 2: Social Cognition Maintains multi-layer adjacency matrices, computes intimacy scores using multi-factor aggregation, and evaluates dynamic trust and risk metrics that factor in content uncertainty, historic satisfaction, language dissonance, and agent risk profiles.
• Module 3: Motivational Decision—ICR² Engine Instantiates a motivation calculus:

$C = \alpha I + \beta N + \gamma R - \delta K$

where $I$ is intimacy, $N$ is novelty (based on the preference-content distance), $R$ is reciprocity (incorporating past social interaction, interest alignment, and personality coupling), and $K$ is risk (content and context-dependent). The dynamic decision threshold $\theta = \theta_0 - \kappa V_t$ modulates action propensity according to affective states.

• Module 4: Behavioral Output Converts the internal drive $C$ into probabilistic action via a logistic function:

$$\Pr(\text{watch}) = \sigma\left(\frac{C - \theta}{\tau_u}\right)$$

with $\tau_u$ representing agent-specific stochasticity. Behavioral events (e.g., consuming content, sharing, rating) trigger updates throughout the cognitive stack, maintaining the closed-loop architecture.

This layered design allows rich, agent-level path dependencies—including memory decay, preference drift, emotion-driven adaptation, and nuanced trust-risk adjustments—enabling the emergence of realistic social phenomena (Zhong et al., 27 May 2025).

3. ICR² Motivational Engine: Psychological Basis and Implementation

The Intimacy–Curiosity–Reciprocity–Risk (ICR²) motivational engine is the central quantitative mechanism governing Sim-User Agent decisions. Built upon Multi-Attribute Utility Theory and empirical social psychology, it explicitly encodes key motivational drivers:

• Intimacy ($I$): Aggregated from the social cognition module, reflects social tie strength through a multi-factor composition.
• Curiosity/Novelty ($N$): Encodes the preference–content divergence, stimulating exploration.
• Reciprocity ($R$): Accumulates historical interaction success, similarity, and psychological complementarity.
• Risk ($K$): Integrates content uncertainty, historical reliability, interpersonal trust, and contextual risk aversion.

The decision process combines these, applying a dynamic threshold linked to affective state. This construct is analytically interpretable, tunable via coefficients $(\alpha, \beta, \gamma, \delta)$, and designed to faithfully replicate empirically observed mood- and context-dependent decision patterns (Zhong et al., 27 May 2025).

4. Integration of Recommendation Algorithms and Socially Mediated Feedback Loops

The system embeds standard collaborative filtering and graph-based recommender algorithms—including Matrix Factorization (MF), MultVAE, and LightGCN. At each simulation round:

1. The recommender network produces candidate sets tailored to the current agent population using agent and item embeddings, modified by the evolving GGBond Graph.
2. Each agent, in turn, evaluates the offers via its cognitive stack, ultimately deciding (stochastically) whether to consume content, rate, or share (with generated natural language feedback).
3. Resulting behaviors—consumption, ratings, shares—drive updates to (a) individual states (memory, emotion, preference), and (b) social edge weights (modifying intimacy, reciprocity, risk/trust).
4. These changes feed into the input for the recommender in the subsequent round, yielding an inherently non-stationary, endogenous feedback process.

This multi-stage, multi-round cycle models both short-term response and long-term behavioral adaption under continual recommender influence. Algorithmic outputs (Recall, NDCG) are tracked alongside emergent behavioral metrics (e.g., satisfaction, rating drift, structural changes), thus facilitating controlled, longitudinal studies of social and system phenomena (Zhong et al., 27 May 2025).

5. Dynamic Evolution and Social Influence in Multi-layer Context

A defining property of the GGBond framework is the real-time evolution of both agent and network parameters. As agents interact (directly via shared content or indirectly via influence propagation), the social graph is updated continuously across layers; for example:

• Personality drift may occur when social influence is successful, modifying both an agent's psychological profile and associated edge weights in the personality layer.
• Content adoption, sharing, and group alignment drive updates in the interest and structural layers, affecting Jaccard similarity and homophily.
• Trust and risk models account for new outcomes, potentially enhancing or weakening ties.

This recurrent edgelayer updating mechanism supports the emergence of phenomena such as structural balance, echo chambers, or network fragmentation—enabling the simulation of complex, real-world social adaptation and providing a robust foundation for the analysis of sociotechnical systems under algorithmic intervention (Zhong et al., 27 May 2025).

6. Implications for Evaluation and Research in Dynamic Recommender and Networked Social Systems

By integrating agent-level cognitive architectures with a dynamically structured, multilayer social graph, the GGBond framework provides a controlled experimental testbed to evaluate:

• The impact of recommendation interventions on long-term user satisfaction, preference drift, echo chamber formation, and social influence trajectories.
• The comparative efficacy and unintended consequences of different recommender algorithms when deployed in a realistic, socially adaptive context.
• The emergence and mitigation of information cocoons or polarization, by simulating targeted interventions at the level of influential agents or network subnetworks.

The comprehensive, iterative co-evolution of agent states, algorithm outputs, and multi-layer social structure sets the stage for empirical investigation well beyond what is accessible via static dataset evaluations, supporting research into complex adaptive systems, algorithmic bias, and the ethics of sociotechnical interventions (Zhong et al., 27 May 2025).

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In sum, the GGBond Multi-layer Social Network Framework establishes a rigorous, extensible paradigm for modeling, simulating, and studying the interactions between social influence, individual cognition, and long-term recommender outcomes in multiplex social environments. The framework’s design—melding multi-layer graphs, cognitive agent architectures, analytically interpretable motivational dynamics, and iterative social feedback—provides a powerful platform for advancing the empirical and theoretical understanding of recommendation systems in dynamic, networked societies.