The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the …
This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods …
A Caimo, A Lomi - Journal of Management, 2015 - journals.sagepub.com
We examine the conditions under which knowledge embedded in advice relations is likely to reach across intraorganizational boundaries and be shared between distant organizational …
This paper compares several imputation methods for missing data in network analysis on a diverse set of simulated networks under several missing data mechanisms. Previous work …
This paper compares several missing data treatment methods for missing network data on a diverse set of simulated networks under several missing data mechanisms. We focus the …
Gossip is universal, and multiple studies have demonstrated that it can have beneficial group-level outcomes when negative reports help identify defectors or norm-violators …
Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have …
We extend the well-known and widely used exponential random graph model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network …
Missing data on network ties are a fundamental problem for network analysis. The biases induced by missing edge data are widely acknowledged. In this paper, we present a new …