Dung T. Nguyen

Sources of misinformation in Online Social Networks: Who to suspect?

Online Social Networks (OSNs) have recently emerged as one of the most effective channels for information sharing and discovery due to their ability of allowing users to read and create new content simultaneously. While this advantage provides users more rooms to decide which content to follow, it also makes OSNs fertile grounds for the wide spread of misinformation which can lead to undesirable consequences. In order to guarantee the trustworthiness of content sharing in OSNs, it is thus essential to have a strategic investigation on the first and foremost concern: the sources of misinformation. In this paper, we study k-Suspector problem which aims to identify the top k most suspected sources of misinformation. We propose two effective approaches namely ranking-based and optimization-based algorithms. We further extend our solutions to cope with the incompleteness of collected data as well as multiple attacks, which mostly occur in reality. Experimental results on real-world datasets show that our approaches achieve competitive detection ratios in a timely manner in comparison with available methods.

On the approximability of positive influence dominating set in social networks

In social networks, there is a tendency for connected users to match each other’s behaviors. Moreover, a user likely adopts a behavior, if a certain fraction of his family and friends follows that behavior. Identifying people who have the most influential effect to the others is of great advantages, especially in politics, marketing, behavior correction, and so on. Under a graph-theoretical framework, we study the positive influence dominating set (PIDS) problem that seeks for a minimal set of nodes

Overlapping Community Structures and Their Detection on Social Networks

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Least cost influence maximization across multiple social networks

such that all other nodes in the network have at least a fraction ρ>0 of their neighbors in

Least Cost Influence in Multiplex Social Networks: Model Representation and Analysis

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Optimal and distributed algorithms for coverage hole healing in hybrid sensor networks

. We also study a different formulation, called total positive influence dominating set (TPIDS), in which even nodes in

An optimal algorithm for coverage hole healing in hybrid sensor networks

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Maximizing circle of trust in online social networks

are required to have a fraction ρ of neighbors inside

New techniques for approximating optimal substructure problems in power-law graphs

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Adaptive approximation algorithms for hole healing in hybrid wireless sensor networks

. We show that neither of these problems can be approximated within a factor of (1−ϵ)lnmax{Δ,|V|1/2}, where Δ is the maximum degree. Moreover, we provide a simple proof that both problems can be approximated within a factor lnΔ+O(1). In power-law networks, where the degree sequence follows a power-law distribution, both problems admit constant factor approximation algorithms. Finally, we present a linear-time exact algorithms for trees.