Version 1
: Received: 22 July 2021 / Approved: 23 July 2021 / Online: 23 July 2021 (17:12:56 CEST)
How to cite:
Xie, L.; Huang, H.; Shen, H.; Du, Q. A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks. Preprints2021, 2021070550
Xie, L.; Huang, H.; Shen, H.; Du, Q. A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks. Preprints 2021, 2021070550
Xie, L.; Huang, H.; Shen, H.; Du, Q. A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks. Preprints2021, 2021070550
APA Style
Xie, L., Huang, H., Shen, H., & Du, Q. (2021). A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks. Preprints. https://doi.org/
Chicago/Turabian Style
Xie, L., Hong Shen and Qing Du. 2021 "A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks" Preprints. https://doi.org/
Abstract
Nowadays, we use social networks such as Twitter, Facebook, WeChat and Weibo as means to communicate with each other and get to know others. Gradually social networks has become indispensable in our everyday life, and we cannot absolutely imagine what the daily life would be like without social networks. Through social networks, we can access friends' opinions and behaviors easily and are influenced by them in turn. Thus, an effective algorithm to find the top-K influential nodes (the problem of influence maximization) in the social network is critical for various downstream tasks such as viral marketing, anticipating natural hazards, reducing gang violence, public opinion supervision etc. Solving the problem of influence maximization in real-world propagation scenarios often involves estimating influence strength (influence probability between two nodes), which cannot directly observed. To estimate influence strength, conventional approaches propose various humanly-devised rules to extract features of user interactions, the effectiveness of which heavily depends on domain expert knowledge. Besides, they are often applicable for special scenarios or specific diffusion models. Consequently, they are difficult to be generalized into different scenarios, diffusion models and even domains. Inspired by the powerful ability of neural networks in the field of representation learning, we design a deep hierarchical network embedding model HGE to map nodes (with attributes) into latent space automatically. In general, HGE takes an attributed social network as the input for learning latent network representation of each node, incorporating hierarchical community structure, node attributes and general network structure into a unified deep generative framework. Then, with the leaned latent representation of each node, we propose a HGE-GA algorithm to predict influence strength and compute the top-K influential nodes through a greedy-based maximization algorithm. Extensive experiments on real-world attributed networks demonstrate the outstanding superiority of the proposed HGE model and HGE-GA algorithm compared with the state-of-the-art methods, verifying the effectiveness of the proposed model and algorithm.
Keywords
Influence maximization; Influence strength; Hierarchical network embedding; Community Structures; Attributed social networks
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
