Metrics for best practice networks analysis

Abstrac

What’s in a Group? Identification of group types for Enterprise Social Network Analytics using SWOOP data

We report on research, carried out in collaboration with SWOOP Analytics, to identify metrics that allow distinguishing groups in Enterprise Social Networks (ESN) according to their activity patterns. The emerging field of ESN Analytics has made inroads into providing metrics and models to measure 1) the health and structural properties of enterprise social networks, as well as 2) the activity pattern and distinct behavioural roles of individual users. What is lacking so far is ESN Analytics at the group level. Yet, groups play an important role in ESNs for organising communication and collabo-ration activity. In this study we carry out explorative research employing cluster analysis to identify metrics that best distinguish a sample of 350 ESN groups from three organisations into distinct types. We identify three metrics as most useful: 1) the Gini coefficient, measuring (un)evenness of user par-ticipation, 2) density, measuring the extent to which users interact with each other, and 3) reciprocity, measuring the response rate to messages within the group. The resulting typology of four groups, broadcast streams, information forums, communities of practice and project teams, will be useful for network managers and group leaders to check how well their group is tracking against intended group activity pattern.SWOOP Analytics Pty Lt

Global Human Resource Metrics

[Excerpt] What is the logic underlying global human resources (HR) measurement in your organization? In your organization, do you measure the contribution of global HR programs to organizational performance? Do you know what is the most competitive employee mix, e.g., proportion of expatriates vs. local employees, for your business units? (How) do you measure the cost and value of the different types of international work performed by your employees? In the globalized economy, organizations increasingly derive value from human resources, or “talent” as we shall also use the term here (Boudreau, Ramstad & Dowling, in press). The strategic importance of the workforce makes decisions about talent critical to organizational success. Informed decisions about talent require a strategic approach to measurement. However, measures alone are not sufficient, for measures without logic can create information overload, and decision quality rests in substantial part on the quality of measurements. An important element of enhanced global competitiveness is a measurement model for talent that articulates the connections between people and success, as well as the context and boundary conditions that affect those connections. This chapter will propose a framework within which existing and potential global HR measures can be organized and understood. The framework reflects the premise that measures exist to support and enhance decisions, and that strategic decisions require a logical connection between decisions about resources, such as talent, and the key organizational outcomes affected by those decisions. Such a framework may provide a useful mental model for both designers and users of HR measures

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

Notions of community quality underlie network clustering. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms -- Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on information recovery metrics. Our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it absolutely superior. Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters

Predicting and Evaluating Software Model Growth in the Automotive Industry

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best

Building Networks of Practice

{Excerpt} Extensive media coverage of applications such as FaceBook, MySpace, and LinkedIn suggests that networks are a new phenomenon. They are not: the first network was born the day people decided to create organizational structures to serve common interests—that is, at the dawn of mankind. However, the last 10–20 years have witnessed rapid intensification and evolution of networking activities, driven of course by information and communication technologies as well as globalization. These make it possible for individuals to exchange data, information, and knowledge; work collaboratively; and share their views much more quickly and widely than ever before. Thus, less and less of an organization’s knowledge resides within its formal boundaries or communities of practice. Knowledge cannot be separated from the networks that create, use, and transform it. In parallel, networks now play significant roles in how individuals, groups, organizations, and related systems operate. They will be even more important tomorrow. Since we can no longer assume that closely knit groups are the building blocks of human activity—or treat these as discrete units of analysis—we need to recognize and interface with less-bounded organizations, from non-local communities to links among websites. We should make certain that knowledge harvested in the external environment is integrated with what exists within, especially in dynamic fields where innovation stems from inter-organizational knowledge sharing and learning. Therefore, the structure and composition of nodes and ties, and how these affect norms and determine usefulness, must become key concerns. This makes the study of networks of practice a prime interest for both researchers and practitioners

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Properties of Healthcare Teaming Networks as a Function of Network Construction Algorithms

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other. Most healthcare service network models have been constructed from patient claims data, using billing claims to link patients with providers. The data sets can be quite large, making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks. To address this issue, we compared the properties of healthcare networks constructed using different algorithms and the 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We found that each algorithm produced networks with substantially different topological properties. Provider networks adhered to a power law, and organization networks to a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and greatly altered measures of vertex prominence such as the betweenness centrality. We identified patterns in the distance patients travel between network providers, and most strikingly between providers in the Northeast United States and Florida. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications for selecting the algorithm best suited to the type of analysis to be performed.Comment: With links to comprehensive, high resolution figures and networks via figshare.co