Computing an Aggregate Edge-Weight Function for Clustering Graphs with Multiple Edge Types

TL;DR

This paper explores methods to determine an optimal aggregate edge-weight function for clustering graphs with multiple edge types, aiming to align the composite metric with ground-truth community structures.

Contribution

It introduces two approaches—inverse problem solving and parameter optimization—to find aggregation functions that improve clustering accuracy.

Findings

Effective aggregation functions can be learned to match ground-truth clusters

Experimental results demonstrate the methods' success on real and synthetic data

The approaches improve community detection in multi-edge-type graphs

Abstract

We investigate the community detection problem on graphs in the existence of multiple edge types. Our main motivation is that similarity between objects can be defined by many different metrics and aggregation of these metrics into a single one poses several important challenges, such as recovering this aggregation function from ground-truth, investigating the space of different clusterings, etc. In this paper, we address how to find an aggregation function to generate a composite metric that best resonates with the ground-truth. We describe two approaches: solving an inverse problem where we try to find parameters that generate a graph whose clustering gives the ground-truth clustering, and choosing parameters to maximize the quality of the ground-truth clustering. We present experimental results on real and synthetic benchmarks.