Victor Ströele

Group and link analysis of multi-relational scientific social networks

Analyzing social networks enables us to detect several inter and intra connections between people in and outside their organizations. We model a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community. Finally, we evaluate the temporal evolution of scientific social networks to suggest/predict new relationships.

A Framework for Provenance Analysis and Visualization

Abstract Data provenance is a fundamental concept in scientific experimentation. However, for their proper understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist in the understanding and use of data provenance using visualization techniques, ontologies and complex networks. The framework capture the provenance data and generates new information using ontologies and provenance graph analysis. The graph is analyzed through complex networks techniques and provide some metrics to help in each node analyzes. The visualization presents and highlights the inferences and results. The framework was used in the E-SECO scientific ecosystem to support the scientific experimentation.

Using Context Elements and Data Provenance to Support Reuse in Scientific Software Ecosystem Platform.

[Background] Managing contextual elements and provenance information plays a key role in the context of scientific experiments. Currently the scientific experimentation process requires support for collaborative and distributed activities. Detailed logging of the steps to produce results, as well as the environment context information could allow scientists to reuse these results in future experiments and reuse the experiment or parts of it in another context. [Objectives] The goal of this paper is to present a provenance and context metadata management approach that support researchers to reuse experiments in a collaborative and distributed platform. [Method] First, the context and provenance management life cycle phases were analyzed, considering existing models. Then it was proposed a conceptual framework to support the analysis of contextual elements and provenance data of scientific experiments. An ontology capable of extracting implicit knowledge in this domain was specified. This approach was implemented in a scientific ecosystem platform. [Results] An initial evaluation shown evidences that this architecture is able to help researchers during the reuse and reproduction of scientific experiments. [Conclusions] Context elements and data provenance, associated with inference mechanisms, can be used to support the reuse in scientific experimentation process.

Rational Erdös number and maximum flow as measurement models for scientific social network analysis

In social network analysis, the detection of communities—composed of people with common interests—is a classical problem. Moreover, people can somehow influence any other in the community, i.e., they can spread information among them. In this paper, two models are proposed considering information diffusion strategies and the identification of communities in a scientific social network built through these two model concepts. The maximum flow-based and the Erdös number-based models are proposed as a measurement to weigh all the relationships between elements. A clustering algorithm (k-medoids) was used for the identification of communities of closely connected people in order to evaluate the proposed models in a scientific social network. Detailed analysis of the obtained scientific communities was conducted to compare the structure of formed groups and to demonstrate the feasibility of the solution. The results demonstrate the viability and effectiveness of the proposed solution, showing that information reaches elements that are not directly related to the element that produces it.

BROAD-RSI – educational recommender system using social networks interactions and linked data

There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.

Analyzing scientific context of researchers and communities by using complex network and semantic technologies

Abstract Social network communities are composed of people with common interests who influence or are influenced by themselves. In the scientific context, Scientific Social Networks are characterized as social networks that represent the social relations established by researchers. Identifying and exploring these relationships are fundamental activities to support scientific experiments. In this study, we aim to discuss the use of complex networks combined with semantic analysis in a network of scientific publications called DBLP. DBLP can be classified as big data, and its use for the analysis of connections and influences among researchers can be considered a context-aware approach. Therefore, in the present study, concepts of complex network analysis are applied to verify the level of influence among researchers, by analyzing the structure of the scientific social network under study and its communities. A bidirectional graph-based model was proposed in order to evaluate the influence of researchers, in addition to algorithms to analyze the network structure and identify scientific communities, using ontological terms and rules, considering the scientific context, and identifying new connections to promote scientific collaboration. For the identification of scientific communities, we proposed an overlapping community detection algorithm, named NetSCAN. A large scientific database (DBLP) together with digital libraries were used to evaluate the model and the algorithms in a historical research experiment. The results point to the viability and effectiveness of the proposed solution.

Topological analysis in scientific social networks to identify influential researchers

Social iterations in the scientific environment can be analyzed to enhance collaboration between researchers. Scientific social networks are complex networks that represent researchers iterations through academic tasks. Analyzing the structure of those networks researchers can establish new relationships and to understand the potential of collaboration of their relationships. In this paper we use topological aspects from a Brazilian scientific social network to identify the key researchers to the collaboration flow. The relationships between researchers are used to provide insights about researchers influence in the network.

Data abstraction and centrality measures to scientific social network analysis

Analyzing social iterations in a scientific environment will assist researchers in expanding their collaborative networks. Scientific social networks represent the researchers social iterations in an academic environment. The analysis of these networks requires a detailed study of their structure and it is important the use of visual resources in order to a better understanding of how the social iterations occur. In this paper we will use centrality metrics and a clustering algorithm to analyze the structure of a Brazilian scientific social network. A scientific social network visualization tool will be used to allow a visual analysis of the collaboration between researchers from different educational institutions.