| Title |
Real-time topic-aware influence maximization using preprocessing
|
|---|---|
| Published in |
Computational Social Networks, November 2016
|
| DOI | 10.1186/s40649-016-0033-z |
| Pubmed ID | |
| Authors |
Wei Chen, Tian Lin, Cheng Yang |
| Abstract |
Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 7% |
| Student > Bachelor | 1 | 2% |
| Student > Doctoral Student | 1 | 2% |
| Student > Master | 1 | 2% |
| Unknown | 38 | 86% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Business, Management and Accounting | 1 | 2% |
| Mathematics | 1 | 2% |
| Unknown | 37 | 84% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 March 2022.
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#4,163,524
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Outputs from Computational Social Networks
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#68,078
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Outputs of similar age from Computational Social Networks
#1
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