# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 3 Experiments and Analysis
### Source Localization Accuracy in Empirical Networks  
The problem of epidemic source localization in empirical networks is discussed below. Nine empirical networks from the real world are selected to verify the performance of the algorithm. Hospital [16]: The data comes from the contact between patients and medical staff in one hospital of Lyon between 6 December 2010 and 10 December. In the network, node represents the patient or medical personnel, edge between two nodes denotes the close contact(including chat, ward inspection, operation, etc.). Workplace [17]: The network consists of the communication among staffs in an office building in France from 23 June 2013 to 3 July 2013. When two staff members have face-to-face chat, an edge is built between the corresponding nodes. ACM [18]: The data set is an exchange of participants' data collected during the 2009 ACM Hypertext conference. All participants who volunteer to participate in the data collection program wear a radio badge that records a data item when two participants get close to each other. School [19]: The original data recorded the contact between all students in a local high school of Marseilles, France in December 2013. During the five-day data collection period, any two students who had face-to-face communication were recorded. Infectious [18]: The human exchange network at the art and science fair in Dublin, Ireland, recorded a data item between INFECTIOUS: STAY AWAY (2009) of participants when they became close to each other. URV [20]: The internal email communication network of Rovira I Virgili university in Tarragona, southern Catalonia, Spain. The nodes in the network include the university's students and faculty members. Yeast [21; 22; 23; 24]: A contact network of proteins within yeast, where node represents proteins  
Figure 6: The algorithm performance with unknown infection probability. (**a,c**) represent the relationship between average error distance and \(\beta\) when \(\tilde{\beta}=0.2\) in ERT and BAT respectively. (**b,d**) represent the relationship between average error distance and \(\beta\) when \(\tilde{\beta}=0.8\) in ERT and BAT respectively. The x-axis in the figure represents the parameter \(\beta\) used in the PESL algorithm and the vertical dotted line denotes the true infection probability \(\tilde{\beta}\) in the SIR model. Reporting probability of nodes is \(\delta=0.3,0.6,0.9\) respectively. and edge denotes the relationships between the two proteins. OpenFlights [25]: The traffic network consists of all the flight information collected by OpenFlights.org. The node in the network represents the airports and edge denotes the flight between them. Sex [26; 27]: The sex trade information is collected by a public forum. The original network is a bipartite graph between sex buyers and sex workers, where the edge denotes the trade between them. In the experiment, we ignore the identity and deal with it as an ordinary network. In above nine empirical networks, Hospital, Workplace, ACM, School and Infectious networks are all collected by the SocioPatterns project [[http://www.sociopatterns.org/](http://www.sociopatterns.org/)]([http://www.sociopatterns.org/](http://www.sociopatterns.org/)) about the close contact among people, Sex network is a network of intimate sexual contact among citizens, URV network represents the telecommunication networks, Yeast and OpenFlights networks reflect the information diffusion among the human being. For the research convenience, only the giant component of these networks is preserved, and the multilateral and self-loop are deleted. The basic properties of the network obtained are shown in Table 3.  
The relation between epidemic source localization accuracy and infection probability with various source localization algorithms in nine empirical networks is shown in Figure 7. The recovery probability in the SIR model was set as \(\gamma=0.1\) and the reporting probability of nodes was set as \(\delta=0.3\).