PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
In: Proceedings of the 15th ACM SIGKDD Interna- tional Conference on Knowledge Discovery And Data Mining
2 Pith papers cite this work. Polarity classification is still indexing.
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A two-stage Leiden+LLP ordering saves 0.3-5.4 bits per edge on poorly ordered graphs across encoders, while new BG/CS/CG encoders improve over BVGraph high-compression by 2-9%.
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On the Limits of PAC Learning of Networks from Opinion Dynamics
PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
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Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study
A two-stage Leiden+LLP ordering saves 0.3-5.4 bits per edge on poorly ordered graphs across encoders, while new BG/CS/CG encoders improve over BVGraph high-compression by 2-9%.