Figure 1.
An example of diffusion network.
Colors differentiate the nodes in different layers. The root node is colored in red and its large outgoing degree indicates the early popularity of the message. The large node in yellow triggers the further spread of this message.
Figure 2.
Statistics of diffusion networks.
(a) The distribution of size of diffusion network. (b) The distribution of width of diffusion network. (c) The distribution of depth of diffusion network. (d) The average number of nodes with respect to the layer of diffusion network.
Figure 3.
Temporal characteristics of information diffusion.
(a) The distribution of time interval between two successive forwarding behaviors in the resolution of five minutes. (b) The distribution of time latency of message forwarding. (c) The averaged hourly activity of users.
Figure 4.
(a) The distribution of W(k), where W(k) denotes the number of users who are k-exposed to a message at certain time. (b) The distribution of R(k), where R(k) denotes the number of users who forward a message directly after being k-exposed to it. (c) The probability of forwarding a message as a function of the number of exposures over all cascades.
Figure 5.
The variation of exposure curve for different kinds of messages.
(a) The probability of forwarding a message with and without embedded URL. (b) The probability of forwarding a message with and without events. (c) The probability of forwarding a message with more than one event and with a single event.
Figure 6.
Two particular cases of exposure curve.
(a) The exposure curve for the event “Foxconn worker falls to death”, in which P(k) increases with the number of exposures. (b) The exposure curve for the event “Wenzhou train collision”, in which P(k) decreases with the number of exposures.
Table 1.
Statistics of three types of structural motifs.
Table 2.
Statistics of temporal patterns.