Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

The origins, development and application of Qualitative Comparative Analysis (QCA): the first 25 Years

A quarter century ago, in 1987, Charles C. Ragin published The Comparative Method, introducing a new method to the social sciences called Qualitative Comparative Analysis (QCA). QCA is a comparative case-oriented research approach and collection of techniques based on set theory and Boolean algebra, which aims to combine some of the strengths of qualitative and quantitative research methods. Since its launch in 1987, QCA has been applied extensively in the social sciences. This review essay first sketches the origins of the ideas behind QCA. Next, the main features of the method, as presented in The Comparative Method, are introduced. A third part focuses on the early applications. A fourth part presents early criticisms and subsequent innovations. A fifth part then focuses on an era of further expansion in political science and presents some of the main applications in the discipline. In doing so, this paper seeks to provide insights and references into the origin and development of QCA, a non-technical introduction to its main features, the path travelled so far, and the diversification of applications.</p

Beyond the Rhetoric: Foundation Strategy

How do foundations maximize their impact? What is the role of strategy? Is your foundation strategic? Are you? This groundbreaking research examines the current state of decision making at large, private, U.S. foundations. Through in-depth interviews with CEOs and program officers, the study examines foundation leaders' view and use of strategy in making decisions. Analysis of their responses reveals four categories of decision makers ranging from nonstrategic to strategic. Beyond the Rhetoric sets the stage for future CEP research on the role of strategy in creating foundation impact, and highlights practical implications for CEOs, trustees, and program staff

Using grounded theory to understand software process improvement: A study of Irish software product companies

Software Process Improvement (SPI) aims to understand the software process as it is used within an organisation and thus drive the implementation of changes to that process to achieve specific goals such as increasing development speed, achieving higher product quality or reducing costs. Accordingly, SPI researchers must be equipped with the methodologies and tools to enable them to look within organisations and understand the state of practice with respect to software process and process improvement initiatives, in addition to investigating the relevant literature. Having examined a number of potentially suitable research methodologies, we have chosen Grounded Theory as a suitable approach to determine what was happening in actual practice in relation to software process and SPI, using the indigenous Irish software product industry as a test-bed. The outcome of this study is a theory, grounded in the field data, that explains when and why SPI is undertaken by the software industry. The objective of this paper is to describe both the selection and usage of grounded theory in this study and evaluate its effectiveness as a research methodology for software process researchers. Accordingly, this paper will focus on the selection and usage of grounded theory, rather than results of the SPI study itself

Necessary and sufficient factors in employee downsizing? A qualitative comparative analysis of lay-offs in France and the UK, 2008-2013

Embedded in the literature on financialization and institutional approaches, this study is an examination of the causal factors of employee downsizing in two institutionally dissimilar settings, France and the UK, using the fuzzy sets variant of Qualitative Comparative Analysis. The findings show that the roughly equivalent use of large-scale lay-offs in the two countries is coupled with strikingly different causal factors. Our argument suggests the importance of complex causation whereby employee downsizing reflects the growing influence of financial considerations in the governance of companies, but its diffusion across countries is shaped by different configurations of institutional arrangements