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Detalhes

Detalhes

  • Nome

    Shazia Tabassum
  • Cluster

    Informática
  • Cargo

    Estudante Externo
  • Desde

    01 abril 2015
002
Publicações

2018

Cover Image, Volume 8, Issue 5

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

Publicação
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Abstract

2018

Social network analysis: An overview

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

Publicação
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

2018

Processing Evolving Social Networks for Change Detection Based on Centrality Measures

Autores
Pereira, FSF; Tabassum, S; Gama, J; de Amo, S; Oliveira, GMB;

Publicação
Studies in Big Data - Learning from Data Streams in Evolving Environments

Abstract

2018

Biased Dynamic Sampling for Temporal Network Streams

Autores
Tabassum, S; Gama, J;

Publicação
Studies in Computational Intelligence

Abstract
Considering the avalanche of evolving data and the memory constraints, streaming networks’ sampling has gained much attention in the recent decade. However, samples choosing data uniformly from the beginning to the end of a temporal stream are not very relevant for temporally evolving networks where recent activities are more important than the old events. Moreover, the relationships also change overtime. Recent interactions are evident to show the current status of relationships, nevertheless some old stronger relations are also substantially significant. Considering the above issues we propose a fast memory less dynamic sampling mechanism for weighted or multi-graph high-speed streams. For this purpose, we use a forgetting function with two parameters that help introduce biases on the network based on time and relationship strengths. Our experiments on real-world data sets show that our samples not only preserve the basic properties like degree distributions but also maintain the temporal distribution correlations. We also observe that our method generates samples with increased efficiency. It also outperforms current sampling algorithms in the area. © 2019, Springer Nature Switzerland AG.

2017

privy: Privacy Preserving Collaboration Across Multiple Service Providers to Combat Telecoms Spam

Autores
Ajmal, M; Bag, S; Tabassum, S; Hao, F;

Publicação
IEEE Transactions on Emerging Topics in Computing

Abstract