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 13th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining
3 Pith papers cite this work. Polarity classification is still indexing.
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SenseWalk is an LLM-powered agent-based simulation system for semantic trajectories that combines LLMs with the social force model, supported by a user interface, quantitative evaluation, and a user study with 12 participants.
Empirical tests on three real networks show Shapley-value node selection for coverage under reachability rules reaches ~0.9 approximation ratio and beats degree baseline, with one case covering half of Cora using 26 nodes.
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SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments
SenseWalk is an LLM-powered agent-based simulation system for semantic trajectories that combines LLMs with the social force model, supported by a user interface, quantitative evaluation, and a user study with 12 participants.
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Sphere of Influence Centrality via Shapley Values: Empirical Approximation and Network Coverage Analysis
Empirical tests on three real networks show Shapley-value node selection for coverage under reachability rules reaches ~0.9 approximation ratio and beats degree baseline, with one case covering half of Cora using 26 nodes.