Emerging criteria for the low-coherence cannot classify category

As suggested by Main et al., to respond to the need for an adaptation of the existing Adult Attachment Interview (AAI) coding system, especially regarding the application to nonnormative samples, this study presents additional criteria that characterize the low-coherence cannot classify (CC) category. Three AAIs were selected from a sample of parents of maltreated children. All transcripts indicated a very low coherence, with no evidence of contradictory insecure discourse strategies. Moreover, global category descriptors were identified, together with specific indices of discourse characteristics and features that highlight the breakdown in reasoning and discourse experienced by the speakers. The aim of the study is to illustrate new criteria to identify and rate a low-coherence CC profile toward the operationalization of this pervasively unintegrated state of mind. Through the definition of additional criteria for low-coherence CC category, our study helps the AAI and its coding system be more flexible and effective when dealing with clinical samples

Exploring continuous organisational transformation as a form of network interdependence

In this paper we examine the problematic area of continuous transformation. We conduct our analysis from three theoretical perspectives: the resource based view, social network theory, and stakeholder theory. We found that the continuous transformation can be explained through the concept of Network Interdependence. This paper describes Network Interdependence and develops theoretical propositions from a synthesis of the three theories. Our contribution of Network Interdependence offers fresh insights into managing complex change and offers new ways of looking at organisational transformation

Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201