Tianyi Pan

Scalable bicriteria algorithms for the threshold activation problem in online social networks

We consider the Threshold Activation Problem (TAP): given social network G and positive threshold T, find a minimum-size seed set A that can trigger expected activation of at least T. We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP to allow the user to control the running time versus accuracy of our algorithm through a parameter α ∊ (0, 1): given η > 0, with probability 1 − η our algorithm returns a solution A with expected activation greater than T — 2αΤ, and the size of the solution A is within factor 1−h 4αΤ + log(T) of the optimal size. The algorithm runs in time O (α⁻² log (n/η) (n + m)|A|), where n, m, refer to the number of nodes, edges in the network. The performance guarantee holds for the general triggering model of internal influence and also incorporates external influence, provided a certain condition is met on the cost-effectivity of seed selection.

Leveraging Social Communities for Optimizing Cellular Device-to-Device Communications

Device-to-device (D2D) communications over the licensed wireless spectrum has been recently proposed as a promising technology to meet the capacity crunch of next generation cellular networks. However, due to the high mobility of cellular devices, establishing and ensuring the success of D2D transmission become a major challenge. To this end, in this paper, a novel framework is proposed to enable devices to form multi-hop D2D connections in an effort to maintain sustainable communication in the presence of device mobility. To solve the problem posed by device mobility, in contrast to existing works, which mostly focus on physical domain information, a durable community-based approach is introduced taking social encounters into context. It is shown that the proposed scheme can derive an optimal solution for time sensitive content transmission while also minimizing the cost that the base station pays in order to incentivize users to participate in D2D. Simulation results show that the proposed social community aware approach yields significant performance gain, in terms of the amount of traffic offloaded from the cellular network to the D2D tier, compared with the classical social-unaware methods.

Pseudo-Separation for Assessment of Structural Vulnerability of a Network

Based upon the idea that network functionality is impaired if two nodes in a network are sufficiently separated in terms of a given metric, we introduce two combinatorial pseudocut problems generalizing the classical min-cut and multi-cut problems. We expect the pseudocut problems will find broad relevance to the study of network reliability. We comprehensively analyze the computational complexity of the pseudocut problems and provide three approximation algorithms for these problems. Motivated by applications in communication networks with strict Quality-of-Service (QoS) requirements, we demonstrate the utility of the pseudocut problems by proposing a targeted vulnerability assessment for the structure of communication networks using QoS metrics; we perform experimental evaluations of our proposed approximation algorithms in this context.

Price Modification Attack and Protection Scheme in Smart Grid

Smart grid addresses the problem of existing power grid’s increasing complexity, growing demand, and requirement for greater reliability through two-way communication and automated residential load control among others. These features also make the smart grid a target for a number of cyber attacks. In this paper, we study the problem of price modification attack (PMA) through fabrication of price messages, which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network. Combining with cascading failure, it ends up with a highly damaging attack. We prove that the problem is nondeterministic polynomial-time-complete and provide its inapproximability. We devise two approaches for the problem, the former deals with maximizing failure of lines with the given resource and then extending the effect with cascading failure, while the later takes cascading potential into account while choosing the lines to fail. We formulate new protection strategy against PMA and this includes two new algorithms, namely bi-level programming with new branching method and an effective heuristic to improve the running time. Empirical results on both IEEE bus data and real network help us evaluate our approaches under various settings of grid parameters.

Threat From Being Social: Vulnerability Analysis of Social Network Coupled Smart Grid

Social networks (SNs) have been gradually applied by utility companies as an addition to smart grid and are proved to be helpful in smoothing load curves and reducing energy usage. However, SNs also bring in new threats to smart grid: misinformation in SNs may cause smart grid users to alter their demand, resulting in transmission line overloading and in turn leading to catastrophic impact to the grid. In this paper, we discuss the interdependence in the SN coupled smart grid and focus on its vulnerability. That is, how much can the smart grid be damaged when misinformation related to it diffuses in SNs? To analytically study the problem, we propose the misinformation attack problem in social-smart grid that identifies the top critical nodes in the SN, such that the smart grid can be greatly damaged when misinformation propagates from those nodes. This problem is challenging as we have to incorporate the complexity of the two networks concurrently. Nevertheless, we propose a technique that can explicitly take into account information diffusion in SN, power flow balance, and cascading failure in smart grid integratedly when evaluating node criticality, based on which we propose various strategies in selecting the most critical nodes. Also, we introduce controlled load shedding as a protection strategy to reduce the impact of cascading failure. The effectiveness of our algorithms is demonstrated by experiments on the IEEE bus test cases as well as the Pegase data set.

Breakdown by Rumors: Vulnerability of D2D Communications from Online Social Networks

In this paper, we study how rumors in Online Social Networks (OSNs) may impact the performance of device-to-device (D2D) communication. As D2D is a new technology, people may choose not to use it when believed in rumors of its negative impacts. Thus, the cellular network with underlaying D2D is vulnerable to OSNs as rumors in OSNs may decrement the throughput of the cellular network in popular content delivery scenarios. To analyze the vulnerability, we introduce the problem of finding the most critical nodes in the OSN such that the throughput of a content delivery scenario is minimized when a rumor starts from those nodes. We then propose an efficient solution to the critical nodes detection problem. The severity of such vulnerability is supported by extensive experiments in various simulation settings, from which we observe up to \(40\%\) reduction in network throughput.