Yilin Shen

On the discovery of critical links and nodes for assessing network vulnerability

The assessment of network vulnerability is of great importance in the presence of unexpected disruptive events or adversarial attacks targeting on critical network links and nodes. In this paper, we study Critical Link Disruptor (CLD) and Critical Node Disruptor (CND) optimization problems to identify critical links and nodes in a network whose removals maximally destroy the networks functions. We provide a comprehensive complexity analysis of CLD and CND on general graphs and show that they still remain NP-complete even on unit disk graphs and power-law graphs. Furthermore, the CND problem is shown NP-hard to be approximated within Ω([(n-k)/(nε)] ) on general graphs with n vertices and k critical nodes. Despite the intractability of these problems, we propose HILPR, a novel LP-based rounding algorithm, for efficiently solving CLD and CND problems in a timely manner. The effectiveness of our solutions is validated on various synthetic and real-world networks.

Dynamic social community detection and its applications.

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current 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

Reactive jamming attacks in multi-radio wireless sensor networks: an efficient mitigating measure by identifying trigger nodes

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A Trigger Identification Service for Defending Reactive Jammers in WSN

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

Adaptive algorithms for detecting critical links and nodes in dynamic networks

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Maximizing circle of trust in online social networks

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

The walls have ears: optimize sharing for visibility and privacy in online social networks

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

are required to have a fraction ρ of neighbors inside

Assessing network vulnerability in a community structure point of view

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