Measuring similarity in co-occurrence data using ego-networks

TL;DR

This paper introduces a new ego-network-based similarity measure for co-occurrence data that outperforms traditional methods and offers a novel perspective by capturing changes in entity centrality.

Contribution

The paper proposes a simple, physically meaningful similarity index based on ego networks, addressing limitations of aggregated network methods in co-occurrence data analysis.

Findings

The new index outperforms traditional network-based similarity measures.

It can sometimes surpass embedding methods in effectiveness.

The measure provides a different dimension for quantifying similarities.

Abstract

The co-occurrence association is widely observed in many empirical data. Mining the information in co-occurrence data is essential for advancing our understanding of systems such as social networks, ecosystem, and brain network. Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach. Here we show that traditional methods based on the aggregated network can bring unwanted in-directed relationship. To cope with this issue, we propose a similarity measure based on the ego network of each entity, which effectively considers the change of an entity's centrality from one ego network to another. The index proposed is easy to calculate and has a clear physical meaning. Using two different data sets, we compare the new index with other existing ones. We find that the new index outperforms the traditional network-based similarity measures, and it can sometimes surpass the embedding method. In the meanwhile, the measure by the new index is weakly correlated with those by other methods, hence providing a different dimension to quantify similarities in co-occurrence data. Altogether, our work makes an extension in the network-based similarity measure and can be potentially applied in several related tasks.