Modeling the Evolution of AI Identity Using Structural Features and Temporal Role Dynamics in Complex Networks

Yahui Lu (), Raihanah M. M. and Ravichandran Vengadasamy
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Yahui Lu: Faculty of Social Sciences and Humanities, Universiti Kebangsaan, Bangi 43600, Malaysia
Raihanah M. M.: Faculty of Social Sciences and Humanities, Universiti Kebangsaan, Bangi 43600, Malaysia
Ravichandran Vengadasamy: Faculty of Social Sciences and Humanities, Universiti Kebangsaan, Bangi 43600, Malaysia

Mathematics, 2025, vol. 13, issue 20, 1-23

Abstract: In increasingly networked environments, artificial agents are required to operate not with fixed roles but with identities that adapt, evolve, and emerge through interaction. Traditional identity modeling approaches, whether symbolic or statistical, fail to capture this dynamic, relational nature. This paper proposes a network-based framework for constructing and analyzing AI identity by modeling interaction, representation, and emergence within complex networks. The goal is to uncover how agent identity can be inferred and explained through structural roles, temporal behaviors, and community dynamics. The approach begins by transforming raw data from three benchmark domain, Reddit, the Interaction Network dataset, and AMine, into temporal interaction graphs. These graphs are structurally enriched via motif extraction, centrality scoring, and community detection. Graph Neural Networks (GNNs), including GCNs, GATs, and GraphSAGE, are applied to learn identity embeddings across time slices. Extensive evaluations include identity coherence, role classification accuracy, and temporal embedding consistency. Ablation studies assess the contribution of motif and temporal layers. The proposed model achieves strong performance across all metrics. On the AMiner dataset, identity coherence reaches 0.854, with a role classification accuracy of 80.2%. GAT demonstrates the highest temporal consistency and resilience to noise. Role trajectories and motif patterns confirm the emergence of stable and transient identities over time. The results validate the fact that the framework is not only associated with healthy quantitative performance but also offers information on behavioral development. The model will be expanded with semantic representations and be more concerned with ethical considerations, such as privacy, fairness, and transparency, to make identity modeling in artificial intelligence systems responsible and trustworthy.

Keywords: AI identity; complex networks; graph neural networks; role dynamics; temporal graphs; identity emergence; network representation learning; multi-agent systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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