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Fairness-aware predictive graph learning in social networks

- Wang, Lei, Yu, Shuo, Febrinanto, Falih, Alqahtani, Fayez, El-Tobely, Tarek

  • Authors: Wang, Lei , Yu, Shuo , Febrinanto, Falih , Alqahtani, Fayez , El-Tobely, Tarek
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 10, no. 15 (2022), p.
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  • Description: Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data. © 2022 by the authors.

An efficient hybrid system for anomaly detection in social networks

- Rahman, Md Shafiur, Halder, Sajal, Uddin, Ashraf, Acharjee, Uzzal

  • Authors: Rahman, Md Shafiur , Halder, Sajal , Uddin, Ashraf , Acharjee, Uzzal
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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  • Description: Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s).

A highway-centric labeling approach for answering distance queries on large sparse graphs

- Jin, Ruoming, Ruan, Ning, Xiang, Yang, Lee, Victor

  • Authors: Jin, Ruoming , Ruan, Ning , Xiang, Yang , Lee, Victor
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: ACM SIGMOD International Conference on Management of Data, 2012 p. 445-456
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  • Description: The distance query, which asks the length of the shortest path from a vertex u to another vertex v, has applications ranging from link analysis, semantic web and other ontology processing, to social network operations. Here, we propose a novel labeling scheme, referred to as Highway-Centric Labeling, for answering distance queries in a large sparse graph. It empowers the distance labeling with a highway structure and leverages a novel bipartite set cover framework/algorithm. Highway-centric labeling provides better labeling size than the state-of-the-art 2-hop labeling, theoretically and empirically. It also offers both exact distance and approximate distance with bounded accuracy. A detailed experimental evaluation on both synthetic and real datasets demonstrates that highway-centric labeling can outperform the state-of-the-art distance computation approaches in terms of both index size and query time. © 2012 ACM.
  • Description: Proceedings of the ACM SIGMOD International Conference on Management of Data
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