Efficient Reverse Influential Community Search Over Social Networks

Temel Kavramlar

The paper proposes an efficient framework to identify a seed community in a social network that has the maximum influence on a user-specified target community, satisfying both structural and keyword constraints.

Özet

The paper introduces the problem of Reverse Influential Community Search (RICS) over social networks. The goal is to find a seed community that has the highest influence on a user-specified target community, while satisfying structural and keyword constraints.
The key highlights and insights are:

The RICS problem is motivated by real-world applications such as online advertising/marketing and disease spread prevention, where we need to identify influential users to target a specific group of users.

The authors propose effective pruning strategies to reduce the search space, including keyword pruning, support pruning, and influence score pruning. These strategies help filter out invalid candidate seed communities efficiently.

An offline pre-computation phase is designed to pre-calculate and index useful information, such as keyword bit vectors, distance vectors, support upper bounds, and boundary influence upper bounds. This pre-computed data is then leveraged during the online RICS query processing.

An efficient online RICS query processing algorithm is developed, which traverses the pre-computed index and applies the proposed pruning strategies to retrieve the optimal seed community.

The paper also introduces a variant problem, Relaxed Reverse Influential Community Search (R2ICS), which returns a subgraph with relaxed structural constraints but having the maximum influence on the target community.

Comprehensive experiments on real-world and synthetic social networks demonstrate the efficiency and effectiveness of the proposed RICS and R2ICS approaches under various parameter settings.

İstatistikler

None.

Alıntılar

None.

Önemli Bilgiler Şuradan Elde Edildi

Reverse Influential Community Search Over Social Networks (Technical Report)

by Qi Wen,Nan Z... : arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01510.pdf

Daha Derin Sorular

How can the proposed RICS and R2ICS approaches be extended to handle dynamic social networks, where the graph structure and user interests may change over time

To extend the proposed RICS and R2ICS approaches to handle dynamic social networks, several strategies can be implemented. Firstly, incorporating real-time data processing techniques to update the graph structure and user interests as they evolve over time is crucial. This can involve utilizing streaming algorithms to continuously analyze incoming data and adjust the community search accordingly. Additionally, implementing incremental algorithms that can efficiently update the influence scores and community structures based on the changing network dynamics is essential. By integrating these dynamic adaptation mechanisms, the RICS and R2ICS approaches can effectively handle the evolving nature of social networks.

What are the potential limitations or drawbacks of the current RICS and R2ICS solutions, and how can they be addressed in future research

While the RICS and R2ICS solutions offer valuable insights into influential community search, there are potential limitations that need to be addressed. One limitation is the scalability of the algorithms when dealing with large-scale social networks, as the computational complexity can increase significantly with network size. To mitigate this, optimizing the algorithms for parallel processing and distributed computing frameworks can enhance scalability. Another drawback is the reliance on predefined keyword sets, which may limit the flexibility of the approach. Future research could focus on developing techniques to automatically extract relevant keywords or topics from user interactions to improve the adaptability of the algorithms.

Beyond online advertising/marketing and disease prevention, what other real-world applications can benefit from the reverse influential community search problem, and how can the techniques be adapted to those domains

The reverse influential community search problem has diverse applications beyond online advertising/marketing and disease prevention. One potential application is in recommendation systems, where identifying influential communities can enhance personalized recommendations for users based on their social interactions. Moreover, in the field of cybersecurity, detecting influential communities can aid in identifying potential threats or malicious activities within social networks. Adapting the RICS and R2ICS techniques to these domains would involve customizing the influence metrics and structural constraints to suit the specific requirements of each application, thereby improving the effectiveness of community search in diverse real-world scenarios.