  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.
