Enhancing community detection using a network weighting strategy

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph tranversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.Comment: 28 pages, 2 figure

Collaboration evaluation methodology for experience capitalization in industrial processes

Collaboration is a key factor that encourages an efficient running of industrial processes. The measurement of the collaboration performance is necessary to allow experience capitalization and reuse in order to support decision making about efficient collaborations in future processes. This article describes a proposition of collaboration and performance evaluation methodology in industrial processes for experience capitalization. For this purpose, a collaboration model is introduced in order to develop an evaluation methodology. Finally, a case study applied to the aeronautical domain is presented to illustrate the methodology and validate the proposal

Characterizing Design Process Interfaces as Organization Networks: Insights for Engineering Systems Management

The engineering design literature has provided guidance on how to identify and analyze design activities and their information dependencies. However, a systematic characterization of process interfaces between engineering design activities is missing, and the impact of structural and compositional aspects of interfaces on process performance is unclear. To fill these gaps, we propose a new approach that characterizes process interfaces as organization networks consisting of people and their interactions when performing interfacing activities. Furthermore, we provide guidance on how to test and interpret the effect of those characteristics on interface problems. As a result, we show how structural and compositional aspects of the organization networks between information-dependent activities provide valuable insights to better manage complex engineering design processes. The proposed approach is applied to the development of a power plant, analyzing 79 process interfaces. The study reveals a relationship between the structure and composition of the process interfaces and reported interface problems. Implications of this approach include the integration of information about process and organization architectures, the systematic identification of key performance metrics associated with interface problems, and improved support for engineering managers by means of a better overview of information flows between activities

The impact of collaboration strategy in the field of innovation on the effectiveness of organizational structure of healthcare institutions

The need for innovative development of healthcare institutions is determined by the necessity to increase the efficiency of organizational processes based on the formation of new models of cooperation, which will make it possible to get access to new technologies and knowledge. The goal of the study is to determine the parameters of the impact of innovative open cooperation strategy and the strategy of innovative closed cooperation of healthcare institutions on the effectiveness of their organizational structure in the context of dissemination and the use of knowledge. Simulation modeling was applied to generate the most effective organizational management structure in the context of innovative cooperation and knowledge exchange within the organizational processes “Inside-out” and “Inside-in”. It is substantiated that the strategies of innovative cooperation “Open Innovation/Closed Innovation” have a significant impact on the organizational structure of management of healthcare institutions in terms of the “degree of centralization” (Dci), “degree of mediation” (Dii), and “degree of centralization of powers” (Dpi). The values of the selected criteria range from 25,52% to 61,50% in the case of Dii, and from 34,53% to 52,63% in the case of Dci, which indicates a higher efficiency of organizational knowledge exchange processes in healthcare institutions, which adhere to the Open Innovation strategy of innovative cooperation. Therefore, there are significant differences in the effectiveness of the management’s organizational structure depending on the degree of openness of innovative cooperation of healthcare institutions. The strategy of innovative openness allows increasing the number and quality of connections in the context of knowledge exchange between the subjects (actors, agents) of the organizational structure (in a broad sense, considering internal and external levels of externality) of healthcare institutions, regardless of the distance between them and the level of similarity

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing