Information, ideas, and diseases, or more generally, contagions, spread over space and time through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a good network structure and individual diffusion pathways are obtained. The advent of rich social, media and locational data allows us to study and model this diffusion process in more detail than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall process is difficult to trace. This propagation is continuous over space and time, where individual transmissions occur at different rates via complex, latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion (STAND) is developed based on the survival model in this research. Both time and spatial distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes are geographic places at a large scale.
Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data. Abstract Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network. To address the effects of increasing homeless populations, planners must understand the size and distribution of their homeless populations, as well as how information and resources are diffused throughout homeless communities. Currently, there is limited publicly available information on the homeless population, e.g. the estimates of the homeless, gathered annually by the US Housing & Urban Development point in time survey. While it is theorized in the literature that the networks of homeless individuals provide access to important information for planners in areas such as health (e.g. needle exchanges) or access (e.g. information diffusion about the location of new shelters), it is almost never measured, and if measured, only at a very small scale. This research addresses the question of how planners can leverage publicly available data on the homeless to better understand their own homeless networks (e.g. relations among the homeless themselves) in a cost-effective and reliable way. To this end, we pro... Abstract A two-layer susceptible-infected-recovered unaware-aware (SIR-UA) epidemic model is presented to analyze the effect of different heterogeneous networks in a population. Random, scale-free, and small-world network topologies are tested to investigate the impact of awareness on the spread of epidemics in a two-layer network with diverse combinations of degree and structure. Susceptible and infected (both unaware and aware) individuals are associated with their neighboring nodes in a social network structure with various degree distributions. In the two-layer SIR-UA epidemic model, a virtual network represents the connections that spread information, while a physical network represents the physical social interactions that spread diseases. We test various combinations of network structures in virtual or physical networks, to understand the impact of information diffusion on the spread of epidemics in a heterogeneous network structure. Then, the effects of awareness on the spread of a disease are discussed. Finally, phase diagrams are illustrated to reveal the final regions covered by an epidemic with various network parameters. We find that a disease spreads less if the virtual social network is more connected than the network of physical connections.