Weili Wu
Biographic Data
| ID | 3924948 |
|---|---|
| NAME | Weili Wu |
| GIVEN NAMES | Weili |
| FAMILY NAME | Wu |
| SIGNATURE | WU W |
| AFFILIATIONS | The University of Texas at Dallas |
| ORCID | 0000-0001-8747-6340 |
| VERIFIED | Yes |
| TOTAL WORKS | 24 |
| TOTAL CITATIONS | 6 |
| WORKS AS AUTHOR | 24 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
Nonlinear Group Influence Maximization Based on Prioritized Double Deep Q-Networks
A Blockchain-Empowered Multiaggregator Federated Learning Architecture in Edge Computing With Deep Reinforcement Learning Optimization
Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure to raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical dep…
ToupleGDD
Aiming at selecting a small subset of nodes with maximum influence on networks, the influence maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, are faced with great difficulties such as theoretical guarantee, time efficiency, generalization, and so on. This makes it unable to adapt to large-sc…
Co-Activity Maximization in Online Social Networks
Social media with online social networks has risen to be a prevalent force in information diffusion and public discourse. Despite its popularity and convenience, social media has been criticized for contributing to societal and ideological polarization as the result of trapping users in an echo chamber and filter bubbles. An emerging line of research focuses on ways to redesign content or link recommendation algorithms to mitigate the polarizatio…
Supplementary Influence Maximization Problem in Social Networks
Due to important applications in viral marketing, influence maximization (IM) has become a well-studied problem. It aims at finding a small subset of initial users so that they can deliver information to the largest amount of users through the word-of-mouth effect. The original IM only considers a singleton item. And the majority of extensions ignore the relationships among different items or only consider their competitive interactions. In reali…
A Greedy Monitoring Station Selection for Rumor Source Detection in Online Social Networks
In monitoring station observation, for the best accuracy of rumor source detection, it is important to deploy monitors appropriately into the network. There are, however, a very limited number of studies on the monitoring station selection. This article will study the problem of detecting a single rumormonger based on an observation of selected infection monitoring stations in a complete snapshot taken at some time in an online social network (OS…
Blockchain-Driven Privacy-Preserving Contact-Tracing Framework in Pandemics
Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage volunta…
Enhanced Group Influence Maximization in Social Networks Using Deep Reinforcement Learning
In contemporary society, groups are pivotal in shaping decisions and actions. The consensus of a majority of members on specific topics often guides the collective decision-making in groups. Group influence maximization (GIM) aims to select $k$ seed users in a network to maximize the number of eventually activated groups. A group is said to be activated if $\beta$ percent of users in this group are activated. This study delves into the strategic …
Influence-Based Community Partition With Sandwich Method for Social Networks
Community partition is an important problem in many areas, such as biology networks and social networks. The objective of this problem is to analyze the relationships among data via the network topology. In this article, we consider the community partition problem under the independent cascade (IC) model in social networks. We formulate the problem as a combinatorial optimization problem that aims at partitioning a given social network into disjo…
Adaptive multi-feature budgeted profit maximization in social networks
Two-Phase Multidocument Summarization Through Content-Attention-Based Subtopic Detection
Multidocument summarization problem deals with extracting main information and ideas from a set of related documents. Solution to this problem is to find an extraction strategy that aims at finding a small subset of sentences that is able to cover the most important information about the whole document set. Although a large number of machine-learning-based methods have shown great promise, the lack of high-quality training data poses an inherent …
Continuous Profit Maximization
Profit maximization (PM) is to select a subset of users as seeds for viral marketing in online social networks, which balances between the cost and the profit from influence spread. We extend PM to formulate a continuous PM under the general marketing strategies (CPM-MS) problem, whose domain is on integer lattices. The objective function of our CPM-MS is dr-submodular, but nonmonotone. It is a typical case of unconstrained dr-submodular maximiza…
Cloud/Edge Computing Resource Allocation and Pricing for Mobile Blockchain
Blockchain can provide a dependable environment for the Internet of Things (IoT), while the high computing power and energy required by blockchain hinder its applications in IoT. Offloading the computation at the resource-limited IoT devices to a cloud/edge computing service provider (CESP) is a feasible solution to the execution of computation-intensive blockchain tasks. The CESP provides computing resources to IoT users with a cloud and multipl…
Recommended psychological crisis intervention response to the 2019 novel coronavirus pneumonia outbreak in China
The novel coronavirus pneumonia (COVID-19) epidemic has brought serious social psychological impact to the Chinese people, especially those quarantined and thus with limited access to face-to-face communication and traditional social psychological interventions. To better deal with the urgent psychological problems of people involved in the COVID-19 epidemic, we developed a new psychological crisis intervention model by utilizing internet technol…
A k-Hop Collaborate Game Model
In online social networks (OSNs), interpersonal communication and information sharing are happening all the time, and it is real time. If a user initiates an activity (game) in OSNs, she will cause a certain impact on her friendship circle naturally, namely, some users in this initiator's friendship circle will be attracted to participate in this activity. Based on such a fact, we design a k-hop collaborated game model, which means that an activi…
Effector Detection Problem in Social Networks
Nowadays, different innovations spread rapidly in online social networks. An activation state can indicate whether each user adopts the target information. The effector detection problem aims to find a way to generate an activation state as close to an observed one as possible. In this article, based on the influence spread, the unconstrained and constrained effector detection problems are proposed. To tackle them, we design two approximation alg…
Optimal Social Influence
A general method of active friending in different diffusion models in social networks
Discount advertisement in social platform
Distributed Rumor Blocking With Multiple Positive Cascades
Misinformation and rumor can spread rapidly and widely through online social networks and therefore rumor controlling has become a critical issue. It is assumed in the existing works that there is a single authority whose goal is to minimize the spread of rumor by generating a positive cascade. In this paper, we study a more realistic scenario when there is multiple positive cascades generated by different agents. For the multiple-cascade diffusi…
Maximizing rumor containment in social networks with constrained time
A novel approach to online social influence maximization
Impact of Geriatric Conditions Versus Medical Diagnoses on ADL Disability Among Nonagenarians and Centenarians
This study emphasizes the importance of geriatric conditions and their association with ADL disability among the oldest-old adult population
Positive influence dominating sets in power-law graphs
Positive influence dominating sets in power-law graphs
A novel approach to online social influence maximization
Impact of Geriatric Conditions Versus Medical Diagnoses on ADL Disability Among Nonagenarians and Centenarians
This study emphasizes the importance of geriatric conditions and their association with ADL disability among the oldest-old adult population
Impact of Geriatric Conditions Versus Medical Diagnoses on ADL Disability Among Nonagenarians and Centenarians
This study emphasizes the importance of geriatric conditions and their association with ADL disability among the oldest-old adult population
Positive influence dominating sets in power-law graphs
Maximizing rumor containment in social networks with constrained time
A novel approach to online social influence maximization
Distributed Rumor Blocking With Multiple Positive Cascades
Misinformation and rumor can spread rapidly and widely through online social networks and therefore rumor controlling has become a critical issue. It is assumed in the existing works that there is a single authority whose goal is to minimize the spread of rumor by generating a positive cascade. In this paper, we study a more realistic scenario when there is multiple positive cascades generated by different agents. For the multiple-cascade diffusi…
Recommended psychological crisis intervention response to the 2019 novel coronavirus pneumonia outbreak in China
The novel coronavirus pneumonia (COVID-19) epidemic has brought serious social psychological impact to the Chinese people, especially those quarantined and thus with limited access to face-to-face communication and traditional social psychological interventions. To better deal with the urgent psychological problems of people involved in the COVID-19 epidemic, we developed a new psychological crisis intervention model by utilizing internet technol…
A k-Hop Collaborate Game Model
In online social networks (OSNs), interpersonal communication and information sharing are happening all the time, and it is real time. If a user initiates an activity (game) in OSNs, she will cause a certain impact on her friendship circle naturally, namely, some users in this initiator's friendship circle will be attracted to participate in this activity. Based on such a fact, we design a k-hop collaborated game model, which means that an activi…
Effector Detection Problem in Social Networks
Nowadays, different innovations spread rapidly in online social networks. An activation state can indicate whether each user adopts the target information. The effector detection problem aims to find a way to generate an activation state as close to an observed one as possible. In this article, based on the influence spread, the unconstrained and constrained effector detection problems are proposed. To tackle them, we design two approximation alg…
Optimal Social Influence
A general method of active friending in different diffusion models in social networks
Discount advertisement in social platform
Two-Phase Multidocument Summarization Through Content-Attention-Based Subtopic Detection
Multidocument summarization problem deals with extracting main information and ideas from a set of related documents. Solution to this problem is to find an extraction strategy that aims at finding a small subset of sentences that is able to cover the most important information about the whole document set. Although a large number of machine-learning-based methods have shown great promise, the lack of high-quality training data poses an inherent …
Continuous Profit Maximization
Profit maximization (PM) is to select a subset of users as seeds for viral marketing in online social networks, which balances between the cost and the profit from influence spread. We extend PM to formulate a continuous PM under the general marketing strategies (CPM-MS) problem, whose domain is on integer lattices. The objective function of our CPM-MS is dr-submodular, but nonmonotone. It is a typical case of unconstrained dr-submodular maximiza…
Cloud/Edge Computing Resource Allocation and Pricing for Mobile Blockchain
Blockchain can provide a dependable environment for the Internet of Things (IoT), while the high computing power and energy required by blockchain hinder its applications in IoT. Offloading the computation at the resource-limited IoT devices to a cloud/edge computing service provider (CESP) is a feasible solution to the execution of computation-intensive blockchain tasks. The CESP provides computing resources to IoT users with a cloud and multipl…
Adaptive multi-feature budgeted profit maximization in social networks
Influence-Based Community Partition With Sandwich Method for Social Networks
Community partition is an important problem in many areas, such as biology networks and social networks. The objective of this problem is to analyze the relationships among data via the network topology. In this article, we consider the community partition problem under the independent cascade (IC) model in social networks. We formulate the problem as a combinatorial optimization problem that aims at partitioning a given social network into disjo…
ToupleGDD
Aiming at selecting a small subset of nodes with maximum influence on networks, the influence maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, are faced with great difficulties such as theoretical guarantee, time efficiency, generalization, and so on. This makes it unable to adapt to large-sc…
Co-Activity Maximization in Online Social Networks
Social media with online social networks has risen to be a prevalent force in information diffusion and public discourse. Despite its popularity and convenience, social media has been criticized for contributing to societal and ideological polarization as the result of trapping users in an echo chamber and filter bubbles. An emerging line of research focuses on ways to redesign content or link recommendation algorithms to mitigate the polarizatio…
Supplementary Influence Maximization Problem in Social Networks
Due to important applications in viral marketing, influence maximization (IM) has become a well-studied problem. It aims at finding a small subset of initial users so that they can deliver information to the largest amount of users through the word-of-mouth effect. The original IM only considers a singleton item. And the majority of extensions ignore the relationships among different items or only consider their competitive interactions. In reali…
A Greedy Monitoring Station Selection for Rumor Source Detection in Online Social Networks
In monitoring station observation, for the best accuracy of rumor source detection, it is important to deploy monitors appropriately into the network. There are, however, a very limited number of studies on the monitoring station selection. This article will study the problem of detecting a single rumormonger based on an observation of selected infection monitoring stations in a complete snapshot taken at some time in an online social network (OS…
Blockchain-Driven Privacy-Preserving Contact-Tracing Framework in Pandemics
Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage volunta…
Enhanced Group Influence Maximization in Social Networks Using Deep Reinforcement Learning
In contemporary society, groups are pivotal in shaping decisions and actions. The consensus of a majority of members on specific topics often guides the collective decision-making in groups. Group influence maximization (GIM) aims to select $k$ seed users in a network to maximize the number of eventually activated groups. A group is said to be activated if $\beta$ percent of users in this group are activated. This study delves into the strategic …
A Blockchain-Empowered Multiaggregator Federated Learning Architecture in Edge Computing With Deep Reinforcement Learning Optimization
Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure to raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical dep…
Nonlinear Group Influence Maximization Based on Prioritized Double Deep Q-Networks
Computer Science (20 works) · Complex Network Analysis Techniques (17 works) · Mathematics (13 works) · Maximization (12 works) · Mathematical optimization (11 works) · Opinion Dynamics and Social Influence (9 works) · Algorithm (8 works) · Approximation algorithm (8 works) · Greedy algorithm (8 works) · Submodular set function (7 works)