In the above paper[1], one author was inadvertently left out of the byline of the paper. The byline should have read as follows:

Recently, in online social networks (OSNs), the least cost influence (LCI) problem has become one of the central research topics. It aims at identifying a minimum number of seed users who can trigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of overlapping users under this circumstances. In this article, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks into a single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves all properties of original networks to achieve high-quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks.

We study the influence maximization problem (IMP) in multiple online social networks (OSNs). In contrast to most of the previous works on influence maximization which only focused on a single network, our work is the first one to evaluate the propagation of influence across multiple networks simultaneously. In this paper, we first propose a general representation for multiple networks using universal ids for the users. We next introduce a powerful coupling scheme which reduces the multiple networks into a single network without changing influencing properties, thereby allowing us to solve the problem in the reduced network. Moreover, the coupling scheme is also an efficient tool to investigate various aspects of the influence propagation on multiple OSNs. The extensive experiments on real-world and synthesized datasets not only confirm the quality of the solution but also reveal interesting insights into the behavior of influence propagation in and across the networks.

The least cost influence (LCI) problem, which asks to identify a minimum number of seed users who can eventually influence a large number of users, has become one of the central research topics recently in online social networks (OSNs). However, existing works mostly focused on a single network while users nowadays often join several OSNs. Thus, it is crucial to investigate the influence in multiplex networks, i.e. the influence is diffused across a set of networks via shared users, in order to obtain the best set of seed users.In this paper, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem in multiplex OSNs by reducing multiplex networks to a single network via various coupling schemes while preserving the most influence propagation properties. Besides the coupling schemes to represent the diffusion process, the framework also includes the influence relay, a new metric to measure the flow of influence inside and between networks. The experiments on both real and synthesized datasets validate the effectiveness of the coupling schemes as well as provide some interesting insights into the process of influence propagation in multiplex networks.

Region coverage and network connectivity are among the most important problems for the quality of service in wireless sensor networks. Unfortunately, due to the sensor failures and hostile environments, such as active volcanic regions or battle fields, the emergence of coverage holes and disconnections among sensors is unavoidable. One way to handle this problem is to deploy mobile sensors in the network, which is called hybrid sensor networks, so that these mobile sensors can be relocated to heal the holes or maintain the network connectivity. However, because of the low-power of mobile sensors, it is extremely challenging to design a fast and effective movement schedule for mobile sensors to (1) maintain both the region coverage and network connectivity at any time, and (2) minimize the moving energy consumption. In this paper, we develop an adaptive algorithm, AHCH algorithm, to adaptively heal the holes with the guarantee of network connectivity without recomputing from scratch. By comparing AHCH algorithm with the optimal solution at each time-slot, we show its expected adaptive approximation ratio as O(log |M|) with |M| mobile sensors in some special cases. In more general cases, we extend our AHCH algorithm to InAHCH and GenAHCH algorithms, handling insufficient mobile sensors as well as disconnected regions, along with the proof of their corresponding theoretical adaptive approximation ratios. The experimental evaluation shows the effectiveness of our proposed algorithms with respect to both low energy consumption and hole healing latency.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.