  Hypergraphs are data structures capable of capturing supra-dyadic relations.
We can use them to model binary relations, but also to model groups of
entities, as well as the intersections between these groups or the contained
subgroups. In previous work, we explored the usage of hypergraphs as an
indexing data structure, in particular one that was capable of seamlessly
integrating text, entities and their relations to support entity-oriented
search tasks. As more information is added to the hypergraph, however, it not
only increases in size, but it also becomes denser, making the task of
efficiently ranking nodes or hyperedges more complex. Random walks can
effectively capture network structure, without compromising performance, or at
least providing a tunable balance between efficiency and effectiveness, within
a nondeterministic universe. For a higher effectiveness, a higher number of
random walks is usually required, which often results in lower efficiency.
Inspired by von Neumann and the neuron in the brain, we propose and study the
usage of node and hyperedge fatigue as a way to temporarily constrict random
walks during keyword-based ad hoc retrieval. We found that we were able to
improve search time by a factor of 32, but also worsen MAP by a factor of 8.
Moreover, by distinguishing between fatigue in nodes and hyperedges, we are
able to find that, for hyperedge ranking tasks, we consistently obtained lower
MAP scores when increasing fatigue for nodes. On the other hand, the overall
impact of hyperedge fatigue was slightly positive, although it also slightly
worsened efficiency.
