Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks

TL;DR

This paper reviews and proposes a fully-weighted, continuous local clustering coefficient for complex networks, emphasizing its advantages over hybrid methods especially in noisy, inferred, or heterogeneous networks.

Contribution

It introduces a new fully-weighted continuous clustering coefficient that meets all necessary criteria and outperforms existing methods in various network conditions.

Findings

Fully-weighted clustering coefficient outperforms hybrid methods.

The new measure is continuous with respect to small weight changes.

It is especially effective for noisy or inferred networks.

Abstract

Weights and directionality of the edges carry a large part of the information we can extract from a complex network. However, many network measures were formulated initially for undirected binary networks. The necessity to incorporate information about the weights led to the conception of the multiple extensions, particularly for definitions of the local clustering coefficient discussed here. We uncover that not all of these extensions are fully-weighted; some depend on the degree and thus change a lot when an infinitely small weight edge is exchanged for the absence of an edge, a feature that is not always desirable. We call these methods ``hybrid'' and argue that, in many situations, one should prefer fully-weighted definitions. After listing the necessary requirements for a method to analyze many various weighted networks properly, we propose a fully-weighted continuous clustering coefficient that satisfies all the previously proposed criteria while also being continuous with respect to vanishing weights. We demonstrate that the behavior and meaning of the Zhang--Horvath clustering and our new continuous definition provide complementary results and significantly outperform other definitions in multiple relevant conditions. Using synthetic and real-world examples, we show that when the network is inferred, noisy, or very heterogeneous, it is essential to use the fully-weighted clustering definitions.