Finding HeavyPaths in Weighted Graphs and a Case-Study on Community Detection

TL;DR

This paper introduces the Heavy Path Problem in weighted graphs, exploring its applications in influence networks and ordering problems, and demonstrates its potential for modeling and solving complex optimization tasks.

Contribution

It defines the Heavy Path Problem, analyzes its significance, and discusses its applications in influence spreading and ordering, providing a foundation for future research.

Findings

Heavy paths represent influential connectivity in networks.

Heavy Path Problem can model complex optimization scenarios.

Potential applications include influence spreading and personalized ordering.

Abstract

A heavy path in a weighted graph represents a notion of connectivity and ordering that goes beyond two nodes. The heaviest path of length l in the graph, simply means a sequence of nodes with edges between them, such that the sum of edge weights is maximum among all paths of length l. It is trivial to state the heaviest edge in the graph is the heaviest path of length 1, that represents a heavy connection between (any) two existing nodes. This can be generalized in many different ways for more than two nodes, one of which is finding the heavy weight paths in the graph. In an influence network, this represents a highway for spreading information from a node to one of its indirect neighbors at distance l. Moreover, a heavy path implies an ordering of nodes. For instance, we can discover which ordering of songs (tourist spots) on a playlist (travel itinerary) is more pleasant to a user or a group of users who enjoy all songs (tourist spots) on the playlist (itinerary). This can also serve as a hard optimization problem, maximizing different types of quantities of a path such as score, flow, probability or surprise, defined as edge weight. Therefore, if one can solve the Heavy Path Problem (HPP) efficiently, they can as well use HPP for modeling and reduce other complex problems to it.