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Wenping Chen

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9 papers

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TCS Journal 2025 Journal Article

Sequential decision based learning method for influence maximization

Zizhen Zhang
Deying Li
Yongcai Wang
Wenping Chen
Yuqing Zhu

Influence maximization (IM) involves choosing an initial group of users within a social network to optimize the expected spread of influence across other users. Recently, learning-based combinatorial optimization (CO) methods have been developed to learn generalized policies for specific CO problems on graphs. However, current learning-based algorithms struggle with diverse diffusion patterns, which restricts their generalization ability. In this paper, we apply reverse influence sampling to simplify the IM problem, reducing it to a stochastic maximum coverage problem using hyperedges. We then model this as a Markov decision process and propose two sequential decision-based learning methods. These methods leverage the symmetry of solutions with respect to sequence order and utilize the submodular reward function. By jointly training on multiple graphs, our approach learns a transferable seed selection policy that generalizes effectively to previously unseen test graphs. Extensive experiments demonstrate that our method outperforms recent learning-based approaches as well as traditional methods on both real and synthetic datasets for the IM problem.

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TCS Journal 2022 Journal Article

Self-stabilizing spanner topology control solutions in wireless ad hoc networks

Xiujuan Zhang
Yongcai Wang
Deying Li
Wenping Chen
Xingjian Ding

Large-scale, self-organizing wireless ad hoc network deployments are being driven by recent developments of the Internet of Things (IoT) to collect information from a vast area or harsh environment efficiently. How to ensure fast routing in sparse topology and after node failure how to ensure that the network keeps topology properties are challenging problems. In this paper, we propose a Self-stabilizing dIrected t-Spanner for Autonomous nodes problem (SISA) and give intuitive solutions in both 2D and 3D space. In the topology construction phase, sparse directed t-spanner topologies, without substantially degrading the path connecting any pair of nodes in the original network, are constructed with a minimum number of messages. In the topology maintenance phase, the self-stabilizing algorithms run in the background to update locally for keeping t-spanner property when some nodes fail. In particular, our solutions have not any central daemon and are completely distributed. Our experimental results demonstrate the effectiveness and efficiency of our proposed solutions.

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TCS Journal 2022 Journal Article

Union acceptable profit maximization in social networks

Guoyao Rao
Yongcai Wang
Wenping Chen
Deying Li
Weili Wu

Online social network has deeply changed our lives, such as the style of communication and business, and hence promotes a lot of researches in social influence. The prior works in social influence mainly consider the influence from the view of individuals. However, in many cases, influencing the most of members of an important group such as the board of directors in a company can bring bigger profit than directly influencing the individuals of the company. We call such high profit group which obeys the vote rule as an union, different from existed targeted influence model, we consider such scenarios to make union acceptable and propose the union acceptable profit problem (UAPM) to choose seeds to maximize the union-acceptable profit, i. e. , maximize the probability of the union being acceptable. The objective of profit in UAPM is #P-hard, and not submodularity or supmodularity. To solve the problem, we propose an efficient estimation method for the objective and design a heuristic algorithm and further a data-driven β ( 1 − 1 ϵ ) -approximation algorithm where β is the data-driven parameter which is related to the input data. At last we evaluate the performance of the algorithms we proposed on effectiveness and efficiency by the experiments in real-world social network datasets.

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TCS Journal 2021 Journal Article

Matching influence maximization in social networks

Guoyao Rao
Yongcai Wang
Wenping Chen
Deying Li
Weili Wu

Influence maximization (IM) is a widely studied problem in social networks, which aims at finding a seed set with limited size that can maximize the expected number of influenced users. However, existing studies haven't considered the matching relationship, which refers to such scenarios that influenced users seek matched partners among the influenced users, such as time matching with friends to watch movie, or matching for opposite sex in the blind date. In this paper, we investigate different matching scenarios and propose online-matching (offline-matching), in which the matching and influence propagation are simultaneous (asynchronous). For the matching result, we introduce two matched types ‘s- m a t c h e d ’, i. e. , i → j and ‘d- m a t c h e d ’, i. e. , i ↔ j. Then, we formulate the matching influence maximization (MM) problem to optimize a limited seed set that maximizes the expected number of matched users. We prove that the MM problem is NP-hard and the computation of the matching influence is #P-hard. Next, we analyze the submodularity of the matching influence. To address the problem, we propose efficient methods OPMM (SAMM) to solve the MM in online-matching (offline-matching) with ( 1 − 1 / e − ϵ ) -approximation ( β ( 1 − 1 / e − ϵ ) -approximation) guarantee. Experiments on the real-world datasets show our algorithms outperform state of the art algorithms in terms of more accurate matching propagation results.

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TCS Journal 2021 Journal Article

Optimizing flight trajectory of UAV for efficient data collection in wireless sensor networks

Chuanwen Luo
Wenping Chen
Deying Li
Yongcai Wang
Hongwei Du
Lidong Wu
Weili Wu

Unmanned Aerial Vehicles (UAVs) are expected to be important components in the upcoming wireless communication field, which are increasingly used as data collectors to gather sensory data from Wireless Sensor Networks (WSNs) due to their high mobility, flexible deployment. Since the storage capacity and lifetime of sensors are increasing with the development of science and technology, sensors can store more and more sensing data about the monitoring area. However, due to the energy limitation of UAVs and a large amount of data carried by sensors, we can not collect all data from WSN within the limited time. Therefore, in this paper, we investigate two problems: (1) without the energy limitation of UAV, how to optimize the trajectory of UAV to minimize the sum of traveling time and data transmission time of UAV while guaranteeing the amount of data collected from each sensor reaches to a certain proportion of the original data, which is called the Minimizing Transportation and Communication Latency (MTCL) problem; (2) given the limited budget of UAV, how to find the optimal trajectory of UAV to maximize the minimum ratio of the collected data to the stored data among all sensors, which is called the Maximizing Data Collection Proportion (MDCP) problem. We first prove that both the problems are NP-Complete. Then we study a special case of the MTCL problem, which is called the MTCL-disjoint problem, in which any pair of data collection areas are disjoint, and we propose an approximation algorithm to solve the MTCL-disjoint problem. Based on the MTCL-disjoint problem, we propose an approximation algorithm for the general MTCL problem. Afterward, an approximation algorithm for the MDCP problem is proposed on the basis of the algorithm for the MTCL problem. Finally, we present numerical results in different scenarios to assess the effectiveness of the proposed algorithms.

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TCS Journal 2020 Journal Article

Efficient scheduling of a mobile charger in large-scale sensor networks

Xingjian Ding
Wenping Chen
Yongcai Wang
Deying Li
Yi Hong

Schedule a mobile charger to replenish energy to sensor nodes for the wireless sensor networks has attracted great attention recently, due to its efficiency and flexibility. Some existing works study the mobile charger scheduling problem by considering that only the depot can recharge or replace the battery for the mobile charger. However, for large-scale wireless sensor networks, the mobile charger is energy inefficient or even may run out of energy during the travel for charging. In this paper, we consider the scenario that there are some service stations in the network area which can be used to replace the battery for the mobile charger, and we study the problem of minimizing the number of used batteries for a mobile charger to charge a wireless sensor network (MBA). We first consider a special case of the MBA problem, in which the depot is the only service station, and we present an approximation algorithm to address it. Then we propose an approximation algorithm for the MBA problem with the assumption that the distance of any two service stations is limited. And finally, we consider the general MBA problem and propose an approximation algorithm. We validate the performance of our algorithms by extensive simulations, and the results show that our proposed algorithms are promising.

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TCS Journal 2020 Journal Article

Target users' activation probability maximization with different seed set constraints in social networks

Ruidong Yan
Hongwei Du
Yi Li
Wenping Chen
Yongcai Wang
Yuqing Zhu
Deying Li

Influence Maximization (IM) over the online social networks have been widely explored in recent years, which selects a seed set from nodes in the network using a limited budget such that the expected number of nodes influenced by the seed set is maximized. However, how to activate a considered set of targeting users T, e. g. , selling a product to a specific target group, is a more practical problem. To address this problem, we respectively propose the Target Users' Activation Probability Maximization with Constraint (TUAPM-WC) problem and the Target Users' Activation Probability Maximization without Constraint (TUAPM-WOC) problem, i. e. , to select a seed set S with/without size constraints such that the activation probabilities of the target users in T are maximized. Considering that the influence will decay during information propagation, we propose a novel and practical Influence Decay Model (IDM) as the information diffusion model. Based on the IDM, we show that the TUAPM-WC and the TUAPM-WOC problems are NP-hard. We also prove that the objective functions of TUAPM-WC and TUAPM-WOC problems are monotone non-decreasing and submodular. On one hand, we employ a Double Greedy Algorithm (DGA) to guarantee a (1/3)-approximation ratio for TUAPM-WOC problem when | S | is unconstrained. On the other hand, we propose a series of algorithms to solve the TUAPM-WC when | S | ≤ b, where b is a positive integer. More specifically, we provide a ( 1 − 1 / e )-approximation Basic Greedy Algorithm (BGA). Furthermore, a speed-up Scalable Algorithm (SA) is proposed for online large social networks. Finally, we run our algorithms by simulations on synthetic and real-life social networks to evaluate the effectiveness and efficiency of the proposed algorithms. Experimental results validate our algorithms' superior to the comparison algorithms.

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TCS Journal 2013 Journal Article

Minimum energy multicast/broadcast routing with reception cost in wireless sensor networks

Deying Li
Zewen Liu
Yi Hong
Wenping Chen
Huan Ma

In this paper, we study the minimum energy multicast/broadcast problem with reception cost in wireless sensor networks. Suppose there are n sensors in the network. Each node v has l ( v ) transmission power levels to choose and its reception cost is B ( v ) if it receives a message. The problem of our concern is: given a multicast (broadcast) request, how to find a multicast (broadcast) tree such that the total energy cost of the multicast tree including transmitting cost and reception cost is minimized. There are two cases for reception cost: one is that for any node v, the reception cost of v only relies on v itself and is irrelevant with its transmitting node, the other is that the reception cost of v relies on not only itself but also its transmitting node. For the first case, we firstly propose a general approximation algorithm MEB-R-G for the broadcast problem. Moreover, for the multicast problem, we propose a general algorithm MEM-R-G and prove its approximation ratio, we also present a greedy algorithm. For the second case, we also propose a general approximation algorithm MEM-RT-G, and prove its approximation ratio.

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TCS Journal 2012 Journal Article

Constrained surface-level gateway placement for underwater acoustic wireless sensor networks

Deying Li
Zheng Li
Wenkai Ma
Hong Chen
Wenping Chen

One approach to guarantee the performance of underwater acoustic sensor networks is to deploy multiple Surface-level Gateways (SGs) at the surface. This paper addresses the connected (or survivable) Constrained Surface-level Gateway Placement (C-SGP ) problem for 3-D underwater acoustic sensor networks. Given a set of underwater sensor nodes (USNs) which are floated at different depths to perform collaborative monitoring tasks over a given region, and a set of candidate locations where SGs may be placed, our objective is to place minimum number of SGs at a subset of candidate locations such that it is connected (or k -connected) from any USN to the base station. We first propose a general algorithm for the connected C-SGP problem and prove its approximation ratio. We also give a constant ratio approximation algorithm for the problem. Second, for the survivable C-SGP problem we also propose a general algorithm and prove its approximation ratio. Finally, we give a constant ratio approximation algorithm for the 2-connected C-SGP problem.

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