The outflow ranking method for weighted directed graphs

A ranking method assigns to every weighted directed graph a (weak) ordering of the nodes. In this paper we axiomatize the ranking method that ranks the nodes according to their outflow using four independent axioms. Besides the well-known axioms of anonymity and positive responsiveness we introduce outflow monotonicity – meaning that in pairwise comparison between two nodes, a node is not doing worse in case its own outflow does not decrease and the other node’s outflow does not increase – and order preservation – meaning that adding two weighted digraphs such that the pairwise ranking between two nodes is the same in both weighted digraphs, then this is also their pairwise ranking in the ‘sum’ weighted digraph. The outflow ranking method generalizes the ranking by outdegree for directed graphs, and therefore also generalizes the ranking by Copeland score for tournaments.

Using Graphs to Visualise and Detect Anomalies in Access Control System Transactions

Physical Access Control Systems are commonly used to secure doors in buildings such as airports, hospitals, government buildings and offices. These systems are designed primarily to provide an authentication mechanism, but they also log each door access as a transaction in a database. Unsupervised learning techniques can be used to detect inconsistencies or anomalies in the mobility data, such as a cloned or forged Access Badge, or unusual behaviour by staff members. In this paper, we present an overview of our method of inferring directed graphs to represent a physical building network and the flows of mobility within it. We demonstrate how the graphs can be used for Visual Data Exploration, and outline how to apply algorithms based on Information Theory to the graph data in order to detect inconsistent or abnormal behaviour.

Developing Approaches for Solving a Telecommunications Feature Subscription Problem

Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one problem is to find an optimal relaxation, which is a generalisation of the feedback vertex set problem on directed graphs, and thus it is an NP-hard task. We present several constraint programming formulations of the problem. We also present formulations using partial weighted maximum Boolean satisfiability and mixed integer linear programming. We study all these formulations by experimentally comparing them on a variety of randomly generated instances of the feature subscription problem.

Structural similarity of directed universal hierarchical graphs: A low computational complexity approach

In the present paper we mainly introduce an efficient approach to measure the structural similarity of so called directed universal hierarchical graphs. We want to underline that directed universal hierarchical graphs can be obtained from generalized trees which are already introduced. In order to classify these graphs, we state our novel graph similarity method. As a main result we notice that our novel algorithm has low computational complexity. (c) 2007 Elsevier Inc. All rights reserved.

Comparing large graphs efficiently by margins of feature vectors

Measuring the structural similarity of graphs is a challenging and outstanding problem. Most of the classical approaches of the so-called exact graph matching methods are based on graph or subgraph isomorphic relations of the underlying graphs. In contrast to these methods in this paper we introduce a novel approach to measure the structural similarity of directed and undirected graphs that is mainly based on margins of feature vectors representing graphs. We introduce novel graph similarity and dissimilarity measures, provide some properties and analyze their algorithmic complexity. We find that the computational complexity of our measures is polynomial in the graph size and, hence, significantly better than classical methods from, e.g. exact graph matching which are NP-complete. Numerically, we provide some examples of our measure and compare the results with the well-known graph edit distance. (c) 2006 Elsevier Inc. All rights reserved.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we present AGWAN (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the AGWAN model to real-world graphs and for generating random graphs from the model. Using the Enron “who communicates with whom” social graph, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to the structure of real-world graphs.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

The impact of nurse-directed protocolised-weaning from mechanical ventilation on nursing practice: A quasi-experimental study

Background:
Internationally, nurse-directed protocolised-weaning has been evaluated by measuring its impact on patient outcomes. The impact on nurses’ views and perceptions has been largely ignored.

Aim:
To determine the change in intensive care nurses’ perceptions, satisfaction, knowledge and attitudes following the introduction of nurse-directed weaning. Additionally, views were obtained on how useful protocolised-weaning was to practice.

Methods:
The sample comprised nurses working in general intensive care units in three university-affiliated hospitals. Nurse-directed protocolised-weaning was implemented in one unit (intervention group); two ICUs continued with usual doctor-led practice (control group). Nurses’ perceptions, satisfaction, knowledge and attitudes were measured by self-completed questionnaires before (Phase I) and after the implementation of nurse-directed weaning (Phase II) in all units.

Results:
Response rates were 79% (n=140n=140) for Phase 1 and 62% (n=132n=132) for Phase II. Regression-based analyses showed that changes from Phase I to Phase II were not significantly different between the intervention and control groups. Sixty-nine nurses responded to both Phase I and II questionnaires. In the intervention group, these nurses scored their mean perceived level of knowledge higher in Phase II (6.39 vs 7.17, p=0.01p=0.01). In the control group, role perception (4.41 vs 4.22, p=0.01p=0.01) was lower and, perceived knowledge (6.03 vs 6.63, p=0.04p=0.04), awareness of weaning plans (6.09 vs 7.06, p=0.01p=0.01) and satisfaction with communication (5.28 vs 6.19, p=0.01p=0.01) were higher in Phase II. The intervention group found protocolised weaning useful in their practice (75%): this was scored significantly higher by junior and senior nurses than middle grade nurses (p=0.02p=0.02).

Conclusion

We conclude that nurse-directed protocolised-weaning had no effect on nurses’ views and perceptions due to the high level of satisfaction which encouraged nurses’ participation in weaning throughout. Control group changes are attributed to a ‘reactive effect’ from being study participants. Weaning protocols provide a uniform method of weaning practice and are particularly beneficial in providing safe guidance for junior staff.