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ARXIV:2603.16080 · GRAPH NEURAL NETWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16080GRAPH NEURAL NETWORKSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks.
Opportunity summary
Pain A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks.
Evidence 0 refs | 0 sources | 17% coverage
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A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction…
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of…
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks.
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10.48550/arXiv.2603.16080A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks.
Abstract
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification. Large-scale datasets like Elliptic have allowed for a rise in the analysis of these systems and in tasks such as fraud detection. In these settings, the amount of transactional context available to each node is determined by the neighborhood aggregation and sampling strategies, yet the interaction between these receptive fields and embedding geometry has received limited attention. In this work, we conduct a controlled comparison of Euclidean and tangent-space hyperbolic GNNs for node classification on a large Bitcoin transaction graph. By explicitly varying the neighborhood while keeping the model architecture and dimensionality fixed, we analyze the differences in two embedding spaces. We further examine optimization behavior and observe that joint selection of learning rate and curvature plays a critical role in stabilizing high-dimensional hyperbolic embeddings. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of hyperbolic GNNs for computational social systems.
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PROBLEM
A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification.
METHOD
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection a...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of hyperbolic GNNs for...
WHY NOW
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of hyperbolic GNNs for computational social systems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A comparative study of Euclidean and hyperbolic GNNs for analyzing Bitcoin transaction networks.
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Graph Neural Networks
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