Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks

Article


Cai, Taotao, Lei, Qi, Sheng, Quan Z., Cui, Ningning, Yang, Shuiqiao, Yang, Jian, Zhang, Wei Emma and Mahmood, Adnan. 2024. "Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks." IEEE Transactions on Knowledge and Data Engineering. 36 (5), pp. 2151-2165. https://doi.org/10.1109/TKDE.2023.3316268
Article Title

Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks

ERA Journal ID17876
Article CategoryArticle
AuthorsCai, Taotao, Lei, Qi, Sheng, Quan Z., Cui, Ningning, Yang, Shuiqiao, Yang, Jian, Zhang, Wei Emma and Mahmood, Adnan
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Journal Citation36 (5), pp. 2151-2165
Number of Pages15
Year2024
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2023.3316268
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/10254336
Abstract

Influence Maximization (IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named, Reconnecting Top-l Relationships (RT l R) query, which aims to find l number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT l R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT l R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT l R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

KeywordsEvolving networks; graph query; influence maximization; link prediction
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460502. Data mining and knowledge discovery
460299. Artificial intelligence not elsewhere classified
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

Byline AffiliationsUniversity of Southern Queensland
Chang'an University, China
Macquarie University
Anhui University, China
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
University of Adelaide
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