Optimizing Social Sharing of Fresh Point-of-Interest Information in Location-Based Social Networks

Основні поняття

The core message of this paper is to develop efficient approximation algorithms with provable performance guarantees for optimizing the social sharing of fresh point-of-interest (PoI) information in location-based social networks (LBSNs).

Анотація

The paper introduces a new combinatorial optimization problem that integrates urban sensing and online social networks to enhance the social sharing of fresh point-of-interest (PoI) information. The authors prove that this problem is NP-hard and existing approximation solutions are not viable.
The key highlights are:

The authors develop a polynomial-time algorithm, Greedy-User-Select (GUS), that guarantees a (1 - m^-2/m * (k-1/k)^k) approximation of the optimum for the static crowdsensing setting, where m is the number of users and k is the budget.

For the mobile crowdsensing setting, where each selected user can move along an n-hop path to sense more PoI, the authors propose an augmentation-adaptive algorithm, Greedy-Path-Select (GPS), that achieves bounded approximation ratios ranging from 1/k(1-1/e) to 1-1/e by adjusting the augmentation factor.

The authors also present a specialized algorithm, Adjusted-GPS, for the case where the sensing graph consists of only user nodes. This algorithm guarantees an approximation ratio of at least 1 - (|V∪V'|-2)/|V∪V'| * (k-1/k)^k, where |V∪V'| is the number of nodes in the sensing graph.

The theoretical results are corroborated by simulations using both synthetic and real-world datasets across different network topologies, demonstrating the practical efficiency of the proposed algorithms.

Статистика

None.

Цитати

None.

Ключові висновки, отримані з

Approximation Algorithms to Enhance Social Sharing of Fresh Point-of-Interest Information

by Songhua Li,L... о arxiv.org 04-16-2024

https://arxiv.org/pdf/2308.13260.pdf

Глибші Запити

How can the proposed algorithms be extended to handle dynamic changes in the sensing and social networks over time

To extend the proposed algorithms to handle dynamic changes in the sensing and social networks over time, we can introduce a mechanism for real-time updates and adaptability. Here are some ways to achieve this:

Dynamic Path Selection: In the mobile crowdsensing setting, the paths recommended to selected users can be dynamically updated based on real-time traffic conditions or user preferences. Algorithms can continuously monitor and adjust the paths to optimize PoI collection and sharing.

Adaptive User Selection: Instead of selecting a fixed set of users initially, the algorithms can periodically reevaluate and update the selected users based on changing network dynamics. This adaptability ensures that the most relevant users are chosen at any given time.

Incremental Learning: Implementing machine learning models that can learn from past data and user interactions can help in predicting future trends and optimizing user selection and path recommendations dynamically.

Event-Based Triggers: Introducing event-based triggers that respond to changes in the network, such as sudden traffic congestion or new PoI information, can prompt the algorithms to reevaluate and adjust their strategies in real-time.

By incorporating these dynamic elements into the algorithms, they can effectively handle changes in the sensing and social networks over time, ensuring optimal performance and adaptability.

What are the potential limitations or drawbacks of the assumption that each selected user can only share PoI information with their immediate neighbors in the social graph

While the assumption that each selected user can only share PoI information with their immediate neighbors in the social graph simplifies the model, it comes with potential limitations and drawbacks:

Limited Reach: Restricting PoI sharing to immediate neighbors may result in limited dissemination of information, especially in large social networks. Important information may not reach users who are not directly connected to the selected users.

Information Silos: Users may be confined to their social circles, leading to the formation of information silos where certain groups have access to specific types of PoI information, limiting diversity and coverage.

Lack of Influence: Users with fewer social connections may have less impact on spreading PoI information, potentially overlooking valuable insights from less-connected individuals.

Delayed Propagation: Information sharing may experience delays as it passes through multiple intermediaries, affecting the timeliness and relevance of the shared PoI data.

To address these limitations, future iterations of the framework could consider implementing mechanisms for indirect sharing, incentivizing users to reach beyond their immediate network, and optimizing information propagation for broader reach and impact.

Can the proposed framework be adapted to other applications beyond location-based social networks, such as crowdsourcing or participatory sensing systems

The proposed framework can indeed be adapted to various applications beyond location-based social networks, such as crowdsourcing or participatory sensing systems. Here's how it can be applied to these domains:

Crowdsourcing Platforms: The algorithms can be utilized to optimize the selection of contributors on crowdsourcing platforms based on their ability to collect and share relevant information. This can enhance the efficiency and effectiveness of crowdsourced tasks.

Participatory Sensing Systems: By integrating the framework into participatory sensing systems, users can be selected to collect and share sensor data based on their location and social connections. This can improve data collection accuracy and coverage in participatory sensing projects.

Disaster Response: The framework can be applied to optimize information sharing during disaster response efforts. By selecting users strategically and guiding them to collect and share critical PoI data, emergency responders can make informed decisions and coordinate rescue operations effectively.

Environmental Monitoring: In environmental monitoring projects, the algorithms can help in selecting users to collect data on environmental parameters and share it with relevant stakeholders. This can aid in tracking environmental changes and promoting sustainability initiatives.

By adapting the framework to these applications, it can enhance data collection, sharing, and decision-making processes in various domains beyond location-based social networks.