Fatigued Random Walks in Hypergraphs: A Neuronal Analogy to Improve Retrieval Performance

by   José Devezas, et al.

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.



There are no comments yet.


page 8


Hypergraph-of-Entity: A General Model for Entity-Oriented Search

The hypergraph-of-entity was conceptually proposed as a general model fo...

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

This paper presents a Kernel Entity Salience Model (KESM) that improves ...

Fatigued PageRank

Connections among entities are everywhere. From social media interaction...

Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding

Knowledge Graphs (KGs) are becoming essential to information systems tha...

Metrics and Ambits and Sprawls, Oh My

A follow-up to my previous tutorial on metric indexing, this paper walks...

On the Emergence of Shortest Paths by Reinforced Random Walks

The co-evolution between network structure and functional performance is...

AttWalk: Attentive Cross-Walks for Deep Mesh Analysis

Mesh representation by random walks has been shown to benefit deep learn...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.