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Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? 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A Case Study on Email Networks with Phishing Synthesis Form Without Function: Agent Social Behavior in the Moltbook Network Hijacking online reviews: sparse manipulation and behavioral buffering in popularity-biased rating systems Integration of Deep Reinforcement Learning and Agent-based Simulation to Explore Strategies Counteracting Information Disorder Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystem Geodesic Semantic Search: Cartographic Navigation of Citation Graphs with Learned Local Riemannian Maps PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework AI Agents Alone Are Not (Yet) Sufficient for Social Simulation Emergent Social Structures in Autonomous AI Agent Networks: A Metadata Analysis of 626 Agents on the Pilot Protocol What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews Social Story Frames: Contextual Reasoning about Narrative Intent and Reception Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation Context-Aware Detection and Victim-Centered Response Generation for Online Harassment in Private Messaging Beyond Leakage and Complexity: Towards Realistic and Efficient Information Cascade Prediction VERA-MH Concept Paper Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media LLM Agents Are the Antidote to Walled Gardens Fast Geometric Embedding for Node Influence Maximization GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs Human-AI Governance (HAIG): A Trust-Utility Approach Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction Leveraging graph neural networks and mobility data for COVID-19 forecasting Opinion de-polarization in social networks with GNNs Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
Characterizing relationships between primary miners in Ethereum by analyzing on-chain transactions
Daniel Rincon Silva · 2020-10-08 · via cs.SI updates on arXiv.org

It is widely accepted that Ethereum mining is highly centralized. Nonetheless, centralization has been mostly characterized by exclusively looking at the influence that independent miners or mining pools can have over the network. Moreover, models of mining behavior assume that miners are either unrelated or only relate via mining pools under highly structured and transparent agreements. If these assumptions and the predictions they entail were to be completely accurate, there would not be any evidence of on-chain transactions between miners, other than the ones expected from mining pool payouts. By looking at on-chain transactions between miners in the Ethereum Network we find that aside from the payouts from mining pools to small miners, there are also transactions that define relationships between mining pools, independent miners and between independent miners and mining pools. Furthermore, by characterizing the topology of the network of miner transactions, we find the emergence of highly connected clusters that control significant amounts of hashing power and exhibit relationships in the opposite direction of what theoretical models predict. This more nuanced characterization of mining centralization can help identify network vulnerabilities and inform protocol redesigns.