Influence maximization algorithm based on cross propagation in location-based social networks
- PDF / 1,330,702 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 41 Downloads / 203 Views
(0123456789().,-volV)(0123456789().,-volV)
Influence maximization algorithm based on cross propagation in location-based social networks Zhen Zhang1 • Zhenyu Zhang2 • Xiaohong Wu2
Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The problem of influence maximization is one of the key issues in social networks. Most of the current studies focus on online social networks while ignoring offline interpersonal relationship networks. Fortunately, the cross propagation considers the characteristics of both the online social networks and offline interpersonal relationship networks, which is more suitable for the real scenarios. In this paper, we design a cross propagation model based on location-based social networks to establish a connection between online social networks and offline interpersonal relationship networks. Where the offline interpersonal relationships are mined by the similarity of POIs, which are based on the encounter characteristics. Then, an influence maximization algorithm based on cross propagation model is provided. The simulation results indicate that the propagation effect of influence in cross propagation networks is better than that only in online social networks, and the proposed algorithm has higher performances in terms of the running time and the sphere of influence. Keywords Influence maximization Social networks LBSNs Cross propagation
1 Introduction The influence maximization is one of the most significant research issues in the field of social networks [1]. The researches show that people’s trust in getting information from the social circle is far beyond that from channels such as television, newspaper, online advertisement, etc. [2]. Therefore, most of people believe that word-of-mouth marketing is the most effective marketing planning [3]. The problem of influence maximization has attracted more attention in both academic world and industrial circles within the background of word-of-mouth marketing.
& Zhen Zhang [email protected] Zhenyu Zhang [email protected] Xiaohong Wu [email protected] 1
College of Information Science and Engineering, Xinjiang ¨ ru¨mqi, China University, U
2
Key Laboratory of Multilingual Information Technology of ¨ ru¨mqi, China Xinjiang, Xinjiang University, U
The influence maximization problem in an online social network is to find a subset of nodes named initial nodes. It uses k to denote the number of the initial nodes, which makes the influence to have the most extensive propagation. Most of the existing studies on influence maximization consider the influence propagation in online social networks while ignoring an important path of offline interpersonal relationship networks in the physical world. The word-of-mouth mode in offline interpersonal relationship networks is usually more authentic and persuasive than online propagation mode. Sometimes, it even has a greater impact on influence maximization. Take the promotion of official accounts as an example, people can not only share official accounts with frie
Data Loading...
