Hybrid sketching saves up to 97% space on dense graphs and 15% on sparse ones by sketching dense cores and storing sparse parts exactly, with new BalloonSketch reducing sketch sizes up to 8x.
The Structure and Function of Complex Networks
6 Pith papers cite this work, alongside 13,819 external citations. Polarity classification is still indexing.
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Network structures of applicant-vacancy links determine matching function forms, with dispersion in search intensities reducing match efficacy and potentially making higher average search counterproductive.
A new modularity function Qbg allows detection of hierarchical communities in bipartite networks at multiple scales by exploiting resolution limits.
A mechanistic model with horizontal gene transfer, new gene capture, genome emergence, and gene loss generates scale-free gene degrees and exponential genome degrees in bipartite networks, closely matching viral and pangenome observations when gene loss rate is set to zero.
A convolution process on a directed network provides a covariance model for SST that respects physical barriers and currents, used to identify thermal hot spots via Monte Carlo RCP projections.
LLMs generate 5P causal graphs from 46 psychotherapy intake transcripts that match human expert graphs in structure and meaning, with moderate clinical usefulness ratings.
citing papers explorer
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Hybrid Sketching Methods for Dynamic Connectivity on Sparse Graphs
Hybrid sketching saves up to 97% space on dense graphs and 15% on sparse ones by sketching dense cores and storing sparse parts exactly, with new BalloonSketch reducing sketch sizes up to 8x.
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The Matching Function: A Unified Look into the Black Box
Network structures of applicant-vacancy links determine matching function forms, with dispersion in search intensities reducing match efficacy and potentially making higher average search counterproductive.
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Hierarchical Community Detection in Bipartite Networks
A new modularity function Qbg allows detection of hierarchical communities in bipartite networks at multiple scales by exploiting resolution limits.
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A generative model for bipartite gene-sharing networks
A mechanistic model with horizontal gene transfer, new gene capture, genome emergence, and gene loss generates scale-free gene degrees and exponential genome degrees in bipartite networks, closely matching viral and pangenome observations when gene loss rate is set to zero.
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A Convolution Process for Sea Surface Temperature Hot-Spot Identification in the Mediterranean Sea
A convolution process on a directed network provides a covariance model for SST that respects physical barriers and currents, used to identify thermal hot spots via Monte Carlo RCP projections.
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InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
LLMs generate 5P causal graphs from 46 psychotherapy intake transcripts that match human expert graphs in structure and meaning, with moderate clinical usefulness ratings.