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Adagraph: Adaptive graph-based algorithms for spam detection in social networks
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-0264-8762
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2017 (English)In: 5th International Conference on Networked Systems, NETYS 2017, Springer Verlag , 2017, p. 338-354Conference paper, Published paper (Refereed)
Abstract [en]

In the past years, researchers developed approaches to detect spam in Online Social Networks (OSNs) such as URL blacklisting, spam traps and even crowdsourcing for manual classification. Although previous work has shown the effectiveness of using statistical learning to detect spam, existing work employs supervised schemes that require labeled training data. In addition to the heavy training cost, it is difficult to obtain a comprehensive source of ground truth for measurement. In contrast to existing work, in this paper we present AdaGraph that is a novel graph-based approach for spam detection. AdaGraph is unsupervised, hence it diminishes the need of labeled training data and training cost. Particularly, AdaGraph effectively detects spam in large-scale OSNs by analyzing user behaviors using graph clustering technique. Moreover, AdaGraph continuously updates detected communities to comply with users dynamic interactions and activities. Extensive experiments using Twitter datasets show that AdaGraph detects spam with accuracy 92.3%. Furthermore, the false positive rate of AdaGraph is less than 0.3% that is less than half of the rate achieved by the state-of-the-art approaches.

Place, publisher, year, edition, pages
Springer Verlag , 2017. p. 338-354
Keyword [en]
Community detection, Distributed systems, Evolving graphs algorithms, Social networks, Unsupervised spam detection, Behavioral research, Graphic methods, Evolving graphs, Graph-based algorithms, Labeled training data, Online social networks (OSNs), Spam detection, State-of-the-art approach, Social networking (online)
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-216569DOI: 10.1007/978-3-319-59647-1_25Scopus ID: 2-s2.0-85019722417ISBN: 9783319596464 OAI: oai:DiVA.org:kth-216569DiVA, id: diva2:1154153
Conference
17 May 2017 through 19 May 2017
Note

QC 20171101

Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2018-02-02Bibliographically approved
In thesis
1. Graph-based Analytics for Decentralized Online Social Networks
Open this publication in new window or tab >>Graph-based Analytics for Decentralized Online Social Networks
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Decentralized Online Social Networks (DOSNs) have been introduced as a privacy preserving alternative to the existing online social networks.  DOSNs remove the dependency on a centralized provider and operate as distributed information management platforms. Current efforts of providing DOSNs are mainly focused on designing the required building blocks for managing the distributed network and supporting the social services (e.g., search, content delivery, etc.). However, there is a lack of reliable techniques for enabling complex analytical services (e.g., spam detection, identity validation, etc.) that comply with the decentralization requirements of DOSNs. In particular, there is a need for decentralized data analytic techniques and machine learning (ML) algorithms that can successfully run on top of DOSNs.

 

In this thesis, we empower decentralized analytics for DOSNs through a set of novel algorithms. Our algorithms allow decentralized analytics to effectively work on top of fully decentralized topology, when the data is fully distributed and nodes have access to their local knowledge only. Furthermore, our algorithms and methods are able to extract and exploit the latent patterns in the social user interaction networks and effectively combine them with the shared content, yielding significant improvements for the complex analytic tasks. We argue that, community identification is at the core of the learning and analytical services provided for DOSNs. We show in this thesis that knowledge on community structures and information dissemination patterns, embedded in the topology of social networks has a potential to greatly enhance data analytic insights and improve results. At the heart of this thesis lies a community detection technique that successfully extracts communities in a completely decentralized manner. In particular, we show that multiple complex analytic tasks, like spam detection and identity validation,  can be successfully tackled by harvesting the information from the social network structure. This is achieved by using decentralized community detection algorithm which acts as the main building block for the community-aware learning paradigm that we lay out in this thesis. To the best of our knowledge, this thesis represents the first attempt to bring complex analytical services, which require decentralized iterative computation over distributed data, to the domain of DOSNs. The experimental evaluation of our proposed algorithms using real-world datasets confirms the ability of our solutions to generate  efficient ML models in massively parallel and highly scalable manner.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 41
Series
TRITA-EECS-AVL ; 2018:4
Keyword
Decentralized Community Detection, Community-aware Learning, Spam Detection, Identity Validation
National Category
Computer Systems
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-222228 (URN)978-91-7729-666-9 (ISBN)
Public defence
2018-03-09, sal C, Electrum building, Kistagången 16, STOCKHOLM, 09:00 (English)
Opponent
Supervisors
Note

QC 20180205

Available from: 2018-02-05 Created: 2018-02-02 Last updated: 2018-02-05Bibliographically approved

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Soliman, AmiraGirdzijauskas, Sarunas

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