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About

About

I am a researcher at LIAAD, INESC TEC Porto. I have successfully concluded a Ph.D. in Informatics Engineering at FEUP, University of Porto under the supervision of Prof. João Gama. I also hold the degrees of Bachelor’s and Master’s in Computer Applications from Kakatiya University (KU) in India. My research focus is on Machine Learning, Big Data Science, Networked Data Streams, Evolving Graphs, and Social Network Analysis. I was an Organizing Committee member of Discovery Challenge at EPIA’2017, High Velocity Mobile Data Mining Workshop at IEEE MDM’2016 and DSIE’2015.

Interest
Topics
Details

Details

  • Name

    Shazia Tabassum
  • Cluster

    Computer Science
  • Role

    Assistant Researcher
  • Since

    01st April 2015
002
Publications

2020

On fast and scalable recurring link's prediction in evolving multi-graph streams

Authors
Tabassum, S; Veloso, B; Gama, J;

Publication
NETWORK SCIENCE

Abstract
The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time. © 2020 Cambridge University Press.

2020

Interconnect bypass fraud detection: a case study

Authors
Veloso, B; Tabassum, S; Martins, C; Espanha, R; Azevedo, R; Gama, J;

Publication
ANNALS OF TELECOMMUNICATIONS

Abstract

2020

Profiling high leverage points for detecting anomalous users in telecom data networks

Authors
Tabassum, S; Azad, MA; Gama, J;

Publication
ANNALS OF TELECOMMUNICATIONS

Abstract
Fraud in telephony incurs huge revenue losses and causes a menace to both the service providers and legitimate users. This problem is growing alongside augmenting technologies. Yet, the works in this area are hindered by the availability of data and confidentiality of approaches. In this work, we deal with the problem of detecting different types of unsolicited users from spammers to fraudsters in a massive phone call network. Most of the malicious users in telecommunications have some of the characteristics in common. These characteristics can be defined by a set of features whose values are uncommon for normal users. We made use of graph-based metrics to detect profiles that are significantly far from the common user profiles in a real data log with millions of users. To achieve this, we looked for the high leverage points in the 99.99th percentile, which identified a substantial number of users as extreme anomalous points. Furthermore, clustering these points helped distinguish malicious users efficiently and minimized the problem space significantly. Convincingly, the learned profiles of these detected users coincided with fraudulent behaviors.

2019

Processing Evolving Social Networks for Change Detection Based on Centrality Measures

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

Publication
Studies in Big Data

Abstract

2018

Cover Image, Volume 8, Issue 5

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

Publication
Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery

Abstract