Network modeling, from the ecological to the molecular scale has become an essential tool
for studying the structure, dynamics and complex behavior of living systems. Graph …

Graph clustering is a prevalent issue associated with social networks, data mining, and
machine learning; its objective is to detect communities or groups in networks. Inspired by …

The emergence of viral variants with altered phenotypes is a public health challenge
underscoring the need for advanced evolutionary forecasting methods. Given extensive …

The network reconstruction task aims to estimate a complex system's structure from various
data sources such as time series, snapshots, or interaction counts. Recent work has …

One of the important issues in social networks is the social communities which are formed by
interactions between its members. Three types of community including overlapping, non …

Modern data lakes are heterogeneous in the vocabulary that is used to describe data. We
study a problem of disambiguation in data lakes: How can we determine if a data value …

Animal movements represent a major risk for the spread of infectious diseases in the
domestic swine population. In this study, we adopted methods from social network analysis …

Community detection is one of the primary problems in social network analysis and this
problem has more challenges in attributed social networks. The purpose of community …

Some consumers have various product demands that are associated with each other.
Although there are methods for discovering the associations among co-purchased products …

This article proposes a neighbors' similarity-based fuzzy community detection (FCD)
method, which we call “NeSiFC.” In the proposed NeSiFC approach, we compute the …