It is indicated that Reddit combines semantic coherence with diversity across organizational levels, a pattern not reproduced by the AI-agent network.
Abstract
Large language models enable the creation of autonomous agents that interact in social environments, raising the question of whether agent-based platforms reproduce the organizational properties of human social networks. We compare Moltbook, a social network populated by AI agents, with early Reddit, focusing on how communities organize and differentiate semantic content, using network analysis and NLP methods to characterize semantic coherence and diversity within and between communities, and their relationship to user activity. We find a systematic difference between the two platforms. Reddit communities show stronger semantic coherence, closer alignment with community names, and greater semantic diversity, with individual communities spanning broader content and communities more differentiated from one another. This combination distinguishes Reddit from Moltbook, whose communities are more homogeneous, less differentiated, and increasingly misaligned with their names over time. Users on Reddit also participate across communities that are more semantically related than those connected by activity in Moltbook. At the interaction level, comment-network motif analysis shows Moltbook dominated by non-reciprocal, broadcast-like exchanges, whereas Reddit shows more reciprocal, chained interaction patterns. These results indicate that Reddit combines semantic coherence with diversity across organizational levels, a pattern not reproduced by the AI-agent network.
Rumor events on social media generate opposing camps whose interaction structure is not captured by spreading models or content detectors. This study describes the camp and bridging structure of three rumor events on Sina Weibo, selected from confirmed cases published by the platform’s rumor-refutation channel. Each event is represented as a multilayer interaction network built from repost, comment, and mention relations. Camps are detected by modularity-based community assignment, and overlap is measured through a fractional membership distribution over communities. Three information-theoretic quantities characterize the structure. In the three events, membership entropy separates committed users from bridging users. Structure-to-stance mutual information measures the alignment between interaction communities and text stance. Cross-layer mutual information measures the consistency of camps across interaction types. A co-opetition matrix of mean edge sentiment describes cooperation within camps and competition between camps and identifies alliance structure. In the three events, membership entropy is bimodal, structure-to-stance mutual information is positive and above a permutation null, and the co-opetition matrix has positive diagonal entries. The three events show three distinct temporal patterns, namely a persistent standoff, a hardening toward a single camp after an official correction, and a reversal with an alliance between two camps. The patterns are recovered under a look-ahead-free temporal scheme. In the three events, bridging users hold higher betweenness centrality than non-bridging users. The results describe cross-camp bridging structure in three rumor cascades and connect the structure to a co-opetition reading of camp relations.
Networks describe systems in biology and beyond, from protein interactions and social relationships to power grids and citation records. Reasoning about such systems requires understanding their structure: which elements are central, which connections bridge separate communities, and how it changes when elements are removed. Although large language models (LLMs) excel at natural language, they struggle with such questions when networks are given as edge lists, sentences or measurement tables, because their structural meaning must be inferred. Here we introduce BioGlyph, which compiles network topology into an interpretable and transferable language of structural roles. BioGlyph combines graph partitioning and structural measurements to identify roles such as hubs, community cores and cross-community connectors, and fixed rules to translate them into a universal vocabulary. The representation describes each element through its structural role, supporting evidence and semantic consequences, leaving both the network and the LLM unchanged. Across twenty networks spanning five domains, BioGlyph substantially improves open LLMs'ability to answer structural reasoning questions, outperforming edge-based, numerical and learned representations by up to 26 percentage points in system accuracy. Ablations show that the gain comes from explicitly encoding structural roles in semantically interpretable terms. The gain is more prominent in dense, community-structured networks and diminishes in sparse networks whose topology is more readily inferred from text. In a budding-yeast protein-interaction network, BioGlyph exposes biological organization: cross-community connectors are enriched for essential genes, whereas peripheral proteins are depleted. BioGlyph thus provides an interpretable representation for both language models and scientists to reason about network structure.
Ucchwas Talukder Utsha, Sakib Mostafa, James Zou et al.· 0 citations
Results demonstrate that jointly incorporating structural, semantic, and sentiment information yields more coherent, semantically aligned communities than structure-only baselines while maintaining practical scalability.
Ghaidaa Al-Sultany, Hayder M. Alash, Raman Kumar et al.· Knowledge and Information Sy...· 0 citations
The system Social$.$Wiki supports the co-creation of interactive social sites, such as those for microblogging, messaging, dating, gaming, ride sharing, and so on, and implements a granular security model to protect personal data in a malleable environment.
T. Henderson, Carmel Schare, Ana Dodik et al.· 0 citations
Developing proposals to execute programs on space telescopes involves networks of astronomers coalescing around ideas and plans for observations. When aggregated, these program level networks allow a collective structure of the overall social network of astronomers using a telescope to be created and analysed. We do this using program level investigator data over the first five cycles of accepted General Observer (GO) programs on the James Webb Space Telescope (JWST). The aggregate network contains 5252 unique astronomers with 144740 connections between them based on their program level participation. We apply a modularity class coefficient to visualize the sub communities that evolved within the aggregate network. Ten dominant sub communities emerge that are shown to correlate at various levels with the JWST scientific categories as defined by the Space Telescope Science Institute (STScI). These sub communities vary in size as well as diversity of countries and institutions represented. Analysis of the research interests of investigators within sub communities reveals that separation along the long axis of the graph is associated with the scale of science performed at community level (AU scale vs kpc Gpc scale). While the social network structures based on institutional affiliation and country of institution are highly centralized, the aggregate network at the investigator level is highly heterarchical and decentralized. It appears to be supportive of cross disciplinary interaction, and institutionalized integration between planetary system and cosmic structure science that is wide and redundant, rather than mediated by a small set of critical brokers. Results have implications for access policy (time allocation processes on telescopes leaving behind a legacy of heterarchical social networks) and access strategy (astronomer need to access networks before accessing telescopes).
Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and interactions through natural language. Unlike classical network models or deep learning approaches, LLMs can simulate context-aware social behavior and language-driven interactions, enabling more realistic modeling of network formation and dynamic social processes. However, existing studies are scattered across different research communities and lack a unified perspective. This survey presents the first comprehensive review of LLMs for social network modeling by organizing the literature into two broad categories: network generative models and dynamic process models. Network generative models are further classified into selection-based and interaction-based approaches, while dynamic process models are categorized into opinion dynamics, information diffusion, and rumor propagation, each with their underlying modeling mechanisms. LLMs enable rich textual social interactions and decision-making, but they also exhibit many limitations, including inherent social biases and prompt sensitivity. We outline these open research challenges and discuss future directions in LLM-based social network modeling.
Shikha Mallick, Alex Thomo, Akrati Saxena· 0 citations
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