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About

About

Recieved my BSc in Mathematics, in 2013 and my MSc in Mathematics and Applications in 2015, both from University of Aveiro. Currently, I am a PhD student of Applied Mathematics (MAP-PDMA), working in the field of social network analysis.

Interest
Topics
Details

Details

  • Name

    Sofia Silva Fernandes
  • Cluster

    Computer Science
  • Role

    External Student
  • Since

    01st September 2016
Publications

2019

Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification

Authors
Silva Fernandes, Sd; T, HF; Gama, J;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Existing approaches for detecting anomalous events in time-evolving networks usually focus on detecting events involving the majority of the nodes, which affect the overall structure of the network. Since events involving just a small subset of nodes usually do not affect the overall structure of the network, they are more difficult to spot. In this context, tensor decomposition based methods usually beat other techniques in detecting global events, but fail at spotting localized event patterns. We tackle this problem by replacing the batch decomposition with a sliding window decomposition, which is further mined in an unsupervised way using statistical tools. Via experimental results in one synthetic and four real-world networks, we show the potential of the proposed method in the detection and specification of local events. © Springer Nature Switzerland AG 2019.

2018

Cover Image, Volume 8, Issue 5

Authors
Tabassum, S; Pereira, FSF; Silva Fernandes, Sd; Gama, J;

Publication
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Abstract

2018

Dynamic graph summarization: a tensor decomposition approach

Authors
Fernandes, S; Fanaee T, H; Gama, J;

Publication
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Due to the scale and complexity of todays' social networks, it becomes infeasible to mine them with traditional approaches. A possible solution to reduce such scale and complexity is to produce a compact (lossy) version of the network that represents its major properties. This task is known as graph summarization, which is the subject of this research. Our focus is on time-evolving graphs, a more complex scenario where the dynamics of the network also should be taken into account. We address this problem using tensor decomposition, which enables us to capture the multi-way structure of the time-evolving network. This property is unique and is impossible to obtain with other approaches such as matrix factorization. Experimental evaluation on five real world networks implies promising results demonstrating that tensor decomposition is quite useful for summarizing dynamic networks.

2018

Social network analysis: An overview

Authors
Tabassum, S; Pereira, FSF; Fernandes, S; Gama, J;

Publication
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Social network analysis (SNA) is a core pursuit of analyzing social networks today. In addition to the usual statistical techniques of data analysis, these networks are investigated using SNA measures. It helps in understanding the dependencies between social entities in the data, characterizing their behaviors and their effect on the network as a whole and over time. Therefore, this article attempts to provide a succinct overview of SNA in diverse topological networks (static, temporal, and evolving networks) and perspective (ego-networks). As one of the primary applicability of SNA is in networked data mining, we provide a brief overview of network mining models as well; by this, we present the readers with a concise guided tour from analysis to mining of networks. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Commercial, Legal, and Ethical Issues > Social Considerations

2017

The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction

Authors
Silva Fernandes, Sd; Tork, HF; da Gama, JMP;

Publication
2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017

Abstract