Why tracing a locality's networked governance is worthwhile

The transition from government to governance brings about a shift in performance evaluation. The focus can no longer be on an individual entity, but must extend into considering how a collective of government and non-government institutions achieves the outcomes sought. How can this evaluation task proceed? While applying the formal methods of social network analysis (SNA) to measuring, analysing and managing networked governance may seem obvious to some, such a solution seems to have been avoided over many decades. SNA tools that non-experts can use have been released in recent past, providing opportunities in learning-by-doing among practitioners and scholars with responsibilities or interests in public sector management. The overarching aim in this paper is to promote adoption of an open-source software tool - NodeXL - as one pathway toward understanding and improving networked governance situations, and toward communicating results to others. It begins by establishing three areas of information needs held by Australia's local governments, where undertaking a pilot study could be useful. They are, local government's real positioning with other decision-makers in the networked governance that is Australian federalism; world better practice in risk governance, given the significant exposure of Australian councils to natural disaster events; and measuring change over time in governance capital, as a component in the capitals approach to measuring sustainable development. Establishing functional and spatial boundaries was a key step in design, with the choice being environment protection and natural resources management in the 350km2 catchment area of the Wonboyn Lake estuary on the far south coast of New South Wales. A Web search of documents containing the terms 'Wonboyn Lake' or 'Wonboyn River' then followed. One hundred and twenty nine documents were retrieved. Analysing their contents led to identifying over two hundred institutional actors either transmitting or receiving knowledge relevant to the locality. Some 420 communications taking place between 1967 and 2011 were identified, and tagged according to year of transmission. The decision-making level within which each institutional actor operated; and whether industry, regulator, external researcher or stakeholder were other characteristics recorded. A 421 x 2 matrix of Wonboyn data was then pasted into the NodeXL template operating on MS Excel 2007/2010. Resource materials downloaded from the Web supported the learning-by-doing element of the pilot study. Four visualisations on networked environmental governance are provided. The first shows unmodified data as a graph in random layout. Its purpose is to provide a benchmark against which some of the SNA procedures available for analysing data can be compared. Then follow three graph layouts, each designed to meet the areas of information need established at the study's beginning. Results suggest, in the author's opinion, any time invested in learning-by-doing with NodeXL will reward those wishing to understand, manage and communicate the complexity that is networked governance. Suggestions on how the Australian Centre for Excellence in Local Government, and practitioners in local councils, could be early adopters of this innovation by using data already available to them are offered, so that they may undertake similar pilot studies

Mediating between practitioner and developer communities: the Learning Activity Design in Education experience

The slow uptake by teachers in post‐compulsory education of new technological tools and technology‐enhanced teaching methods may be symptomatic of a general split in the e‐learning community between development of tools, services and standards, and research into how teachers can use these most effectively (i.e. between the teaching practitioner and technical developer communities). This paper reflects on the experience of transferring knowledge and understanding between these two communities during the Learning Activity Design in Education project funded by the UK Joint Information Systems Committee. The discussion is situated within the literature on ‘mediating representations’ and ‘mediating artefacts’, and shows that the practical operation of mediating representations is far more complex than previously acknowledged. The experience suggests that for effective transfer of concepts between communities, the communities need to overlap to the extent that a single representation is comprehensible to both. This representation may be viewed as a boundary object that is used to negotiate understanding. If the communities do not overlap a chain of intermediate representations and communities may be necessary. Finally, a tentative distinction is drawn between mediating representations and mediating artefacts, based not in the nature of the resources, but in their mode and context of use

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Classification in Networked Data: A Toolkit and a Univariate Case Study

This paper1 is about classifying entities that are interlinked with entities for which the class is known. After surveying prior work, we present NetKit, a modular toolkit for classification in networked data, and a case-study of its application to networked data used in prior machine learning research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier, a relational classifier, and a collective inference procedure. Various existing node-centric relational learning algorithms can be instantiated with appropriate choices for these components, and new combinations of components realize new algorithms. The case study focuses on univariate network classification, for which the only information used is the structure of class linkage in the network (i.e., only links and some class labels). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate that very simple network-classification models perform quite well—well enough that they should be used regularly as baseline classifiers for studies of learning with networked data. The simplest method (which performs remarkably well) highlights the close correspondence between several existing methods introduced for different purposes—that is, Gaussian-field classifiers, Hopfield networks, and relational-neighbor classifiers. The case study also shows that there are two sets of techniques that are preferable in different situations, namely when few versus many labels are known initially. We also demonstrate that link selection plays an important role similar to traditional feature selectionNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

The Subject Specialist in Higher Education - A Review of the Literature

Review of the changing role of the subject librarian over the perio

The category proliferation problem in ART neural networks

This article describes the design of a new model IKMART, for classification of documents and their incorporation into categories based on the KMART architecture. The architecture consists of two networks that mutually cooperate through the interconnection of weights and the output matrix of the coded documents. The architecture retains required network features such as incremental learning without the need of descriptive and input/output fuzzy data, learning acceleration and classification of documents and a minimal number of user-defined parameters. The conducted experiments with real documents showed a more precise categorization of documents and higher classification performance in comparison to the classic KMART algorithm.Web of Science145634

Modeling Complex Networks For (Electronic) Commerce

NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Integration of BPM systems

New technologies have emerged to support the global economy where for instance suppliers, manufactures and retailers are working together in order to minimise the cost and maximise efficiency. One of the technologies that has become a buzz word for many businesses is business process management or BPM. A business process comprises activities and tasks, the resources required to perform each task, and the business rules linking these activities and tasks. The tasks may be performed by human and/or machine actors. Workflow provides a way of describing the order of execution and the dependent relationships between the constituting activities of short or long running processes. Workflow allows businesses to capture not only the information but also the processes that transform the information - the process asset (Koulopoulos, T. M., 1995). Applications which involve automated, human-centric and collaborative processes across organisations are inherently different from one organisation to another. Even within the same organisation but over time, applications are adapted as ongoing change to the business processes is seen as the norm in today’s dynamic business environment. The major difference lies in the specifics of business processes which are changing rapidly in order to match the way in which businesses operate. In this chapter we introduce and discuss Business Process Management (BPM) with a focus on the integration of heterogeneous BPM systems across multiple organisations. We identify the problems and the main challenges not only with regards to technologies but also in the social and cultural context. We also discuss the issues that have arisen in our bid to find the solutions

Learning Collective Behavior in Multi-relational Networks

With the rapid expansion of the Internet and WWW, the problem of analyzing social media data has received an increasing amount of attention in the past decade. The boom in social media platforms offers many possibilities to study human collective behavior and interactions on an unprecedented scale. In the past, much work has been done on the problem of learning from networked data with homogeneous topologies, where instances are explicitly or implicitly inter-connected by a single type of relationship. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, networked data extracted from social media, web pages, and bibliographic databases can contain entities of multiple classes and linked by various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. Learning the collective behavior and interactions in heterogeneous networks becomes much more complex. The contribution of this dissertation include 1) two classification frameworks for identifying human collective behavior in multi-relational social networks; 2) unsupervised and supervised learning models for relationship prediction in multi-relational collaborative networks. Our methods improve the performance of homogeneous predictive models by differentiating heterogeneous relations and capturing the prominent interaction patterns underlying the network structure. The work has been evaluated in various real-world social networks. We believe that this study will be useful for analyzing human collective behavior and interactions specifically in the scenario when the heterogeneous relationships in the network arise from various causal reasons