Efficiently Discovering Hammock Paths from Induced Similarity Networks

TL;DR

This paper introduces an algorithmic framework for discovering hammock paths in similarity networks, enabling exploration of connections in data such as movies, biomedical literature, and clinical trials.

Contribution

The paper presents a novel exploratory algorithm for traversing generalized hammock paths in similarity networks, with heuristics for efficient path discovery.

Findings

Effective in discovering meaningful paths in diverse datasets

Demonstrates potential for unstructured knowledge discovery

Applicable to various domains like movies, biomedical literature, and clinical trials

Abstract

Similarity networks are important abstractions in many information management applications such as recommender systems, corpora analysis, and medical informatics. For instance, by inducing similarity networks between movies rated similarly by users, or between documents containing common terms, and or between clinical trials involving the same themes, we can aim to find the global structure of connectivities underlying the data, and use the network as a basis to make connections between seemingly disparate entities. In the above applications, composing similarities between objects of interest finds uses in serendipitous recommendation, in storytelling, and in clinical diagnosis, respectively. We present an algorithmic framework for traversing similarity paths using the notion of `hammock' paths which are generalization of traditional paths. Our framework is exploratory in nature so that, given starting and ending objects of interest, it explores candidate objects for path following, and heuristics to admissibly estimate the potential for paths to lead to a desired destination. We present three diverse applications: exploring movie similarities in the Netflix dataset, exploring abstract similarities across the PubMed corpus, and exploring description similarities in a database of clinical trials. Experimental results demonstrate the potential of our approach for unstructured knowledge discovery in similarity networks.