← 研踪 PaperTrailarXiv:2611.08812 · v1
Dynamic signatures of collective knowledge through navigation in a network with semantic structure
首次提交 2026-11-06 08:08
版本更新 2026-11-06 08:08
雷达观测 2026-11-09 10:16
摘要
What distinguishes how humans navigate an information network like Wikipedia from what the underlying graph structure and language statistics alone predict? To study human collective navigation, we analyze 70,770 human trajectories through Wikispeedia, an online game where players navigate between articles to reach a target. To compare groups of trajectories rather than individual ones, we merge all trajectories leaving or entering a given article into a Flow Tree. Flow Trees compress collections of trajectories into hierarchies of group splits. We build forward and reverse Flow Trees for 815 of the most commonly traversed articles, representing divergence and convergence, respectively. We compare humans against four random-walker null models, either unweighted or weighted by graph structure, word frequency, or semantic similarity to target. Human Flow Trees are deeper with longer trunks and later splits, indicating players stay together longer than random walkers, whose Flow Trees tend to fan out immediately. An article's degree predicts 51.3-70.9% of the variation in Flow Tree branching factor for random walkers, but only 24.1-36.2% for humans. Taking what graph structure fails to predict as a two-dimensional coordinate system, we define four article types (beacons, funnels, forks, and corridors), distinguished by how humans enter and exit them. These human-defined types are not explained by out-degree or title-word frequency are, and are not recovered by any random walker. A fifth null model, built from first-order human click preferences, partially reproduces human Flow Tree shape but differs significantly in how much of that shape degree explains. We consider collective knowledge to be the structure shared by players who never communicate, beyond what graph structure imposes. It comprises first-order click preferences and a significant component that extends across clicks.
作者备注：24 pages, 9 figures, submitted to PLOS ONE
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v1 · 2026-11-06 08:08 · 最新观测版本
Dynamic signatures of collective knowledge through navigation in a network with semantic structure
What distinguishes how humans navigate an information network like Wikipedia from what the underlying graph structure and language statistics alone predict? To study human collective navigation, we analyze 70,770 human trajectories through Wikispeedia, an online game where players navigate between articles to reach a target. To compare groups of trajectories rather than individual ones, we merge all trajectories leaving or entering a given article into a Flow Tree. Flow Trees compress collections of trajectories into hierarchies of group splits. We build forward and reverse Flow Trees for 815 of the most commonly traversed articles, representing divergence and convergence, respectively. We compare humans against four random-walker null models, either unweighted or weighted by graph structure, word frequency, or semantic similarity to target. Human Flow Trees are deeper with longer trunks and later splits, indicating players stay together longer than random walkers, whose Flow Trees tend to fan out immediately. An article's degree predicts 51.3-70.9% of the variation in Flow Tree branching factor for random walkers, but only 24.1-36.2% for humans. Taking what graph structure fails to predict as a two-dimensional coordinate system, we define four article types (beacons, funnels, forks, and corridors), distinguished by how humans enter and exit them. These human-defined types are not explained by out-degree or title-word frequency are, and are not recovered by any random walker. A fifth null model, built from first-order human click preferences, partially reproduces human Flow Tree shape but differs significantly in how much of that shape degree explains. We consider collective knowledge to be the structure shared by players who never communicate, beyond what graph structure imposes. It comprises first-order click preferences and a significant component that extends across clicks.
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