Uncovering the structure and temporal dynamics of information propagation
Bibliographic Data
| ID | 7994085 |
|---|---|
| Authors | Manuel Gomez-Rodriguez (0000-0003-3930-1161, Max Planck Institute for Intelligent Systems), MANUEL GOMEZ RODRIGUEZ, Jure Leskovec (0000-0002-5411-923X, Stanford University), David Balduzzi (0000-0002-1466-7864, Max Planck Institute for Intelligent Systems), Bernhard Schölkopf (0000-0002-8177-0925, Max Planck Institute for Intelligent Systems) |
| Year | 2014 |
| Volume | 2 |
| Issue | 1 |
| Pages | 26-65 |
| Publication date | 2014-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2014.3 |
| OpenAlex | W2070645266 |
| Language | EN |
| Citations received | 5 |
| References cited | 30 |
Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites
Data mining · Data science · Diffusion · Inference · Information cascade · Mainstream · Node (physics · Statistics · Complex Network Analysis Techniques · Computer Science · Human Mobility and Location-Based Analysis · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence · Theoretical Computer Science
Convex Optimization
Cost-effective outbreak detection in networks
Chapter 36 Large sample estimation and hypothesis testing
Identifying Influential and Susceptible Members of Social Networks
Maximizing the spread of influence through a social network
A Stochastic Approximation Method
Differences in the mechanics of information diffusion across topics
The scaling laws of human travel
Emergence of Scaling in Random Networks
Influentials, Networks, and Public Opinion Formation
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,63 |
| Citation span | 2018 - 2026 (9) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |