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Publicações

Publicações por Mário Miguel Cordeiro

2015

Streaming networks sampling using top-K networks

Autores
Sarmento, R; Cordeiro, M; Gama, J;

Publicação
ICEIS 2015 - 17th International Conference on Enterprise Information Systems, Proceedings

Abstract
The combination of top-K network representation of the data stream with community detection is a novel approach to streaming networks sampling. Keeping an always up-to-date sample of the full network, the advantage of this method, compared to previous, is that it preserves larger communities and original network distribution. Empirically, it will also be shown that these techniques, in conjunction with community detection, provide effective ways to perform sampling and analysis of large scale streaming networks with power law distributions.

2018

Incremental TextRank - Automatic Keyword Extraction for Text Streams

Autores
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publicação
Proceedings of the 20th International Conference on Enterprise Information Systems, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Volume 1.

Abstract
Text Mining and NLP techniques are a hot topic nowadays. Researchers thrive to develop new and faster algorithms to cope with larger amounts of data. Particularly, text data analysis has been increasing in interest due to the growth of social networks media. Given this, the development of new algorithms and/or the upgrade of existing ones is now a crucial task to deal with text mining problems under this new scenario. In this paper, we present an update to TextRank, a well-known implementation used to do automatic keyword extraction from text, adapted to deal with streams of text. In addition, we present results for this implementation and compare them with the batch version. Major improvements are lowest computation times for the processing of the same text data, in a streaming environment, both in sliding window and incremental setups. The speedups obtained in the experimental results are significant. Therefore the approach was considered valid and useful to the research community. Copyright

2018

Evolving Networks and Social Network Analysis Methods and Techniques

Autores
Cordeiro, M; Sarmento, RP; Brazdil, P; Gama, J;

Publicação
Social Media and Journalism - Trends, Connections, Implications

Abstract

2019

Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

Autores
Cordeiro, M; Sarmento, RP; Brazdil, P; Kimura, M; Gama, J;

Publicação
Complex Networks and Their Applications VIII - Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.

Abstract
Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks. © 2020, Springer Nature Switzerland AG.

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