Finding Route Hotspots in Large Labeled Networks

TL;DR

This paper introduces a novel approach to identify route hotspots in large labeled networks by leveraging route patterns, proposing a scalable algorithm with an efficient index structure, and demonstrating its effectiveness on real datasets.

Contribution

It is the first to define and address the problem of finding route hotspots in large labeled networks, introducing the FastRH algorithm and RH-Index for scalable analysis.

Findings

FastRH effectively prunes pattern search space.

RH-Index enables efficient hotspot querying.

Method scales well on real-world datasets.

Abstract

In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly connected owing to the similar characteristics, such as common habits and location proximity. In this paper, we investigate the formation of hotspots from an alternative perspective that considers the routes along the network paths as the auxiliary information, and attempt to find the route hotspots in large labeled networks. A route hotspot is a cohesive subgraph that is covered by a set of routes, and these routes correspond to the same sequential pattern consisting of vertices' labels. To the best of our knowledge, the problem of Finding Route Hotspots in Large Labeled Networks has not been tackled in the literature. However, it is challenging as counting the number of hotspots in a network is #P-hard. Inspired by the observation that the sizes of hotspots decrease with the increasing lengths of patterns, we prove several anti-monotonicity properties of hotspots, and then develop a scalable algorithm called FastRH that can use these properties to effectively prune the patterns that cannot form any hotspots. In addition, to avoid the duplicate computation overhead, we judiciously design an effective index structure called RH-Index for storing the hotspot and pattern information collectively, which also enables incremental updating and efficient query processing. Our experimental results on real-world datasets clearly demonstrate the effectiveness and scalability of our proposed methods.