Do Social Networks Solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.Com

Seth Freedman, Ginger Zhe Jin
2008 Social Science Research Network  
This paper proposes a holistic view of a network organization's computing environment to examine computer virus propagation patterns. We empirically examine a large-scale organizational network consisting of both social network and technological network. By applying information retrieval techniques, we map nodes in the social network to nodes in the technological network to construct the composite network of the organization. We apply social network analysis to study the topologies of social
more » ... technological networks in this organization. We statistically test the impact of the interplay between social and technological network on computer virus propagation using a susceptible-infective-recovered epidemic process. We find that computer viruses propagate faster but reach lower level of infection through technological network than through social network, and viruses propagate the fastest and reach the highest level of infection through the composite network. Overlooking the interplay of social network and technological network underestimates the virus propagation speed and the scale of infection. Key words: social network analysis, interplay between social and technological networks, computer viruses 1 We gratefully acknowledge financial support from the NET Institute (www.netinst.org) and the Kauffman Foundation. 3 4 since the first personal computer virus "Brain" surfaced in early 1986. Brain, a boot sector virus spreading through infected floppy disks, didn't spread quickly. Nor did it cause much harm. However, by showing how destructive self-replicating programs could do, Brain heralded a new era of more devastating computer viruses. Computer viruses pose a critical threat to computer users and organizations causing massive expenses in damages. It is estimated by Computer Economics that the total worldwide financial losses from malware are on average $12.18 billion per year in the period from 1999 to 2006 (Computer Economics 2007). With the ubiquitous presence of Internet, computer viruses develop into thousands of variants which differ in their infection mechanism, propagation mechanism, destructive payload and other features. From the propagation mechanism point of view, viruses can propagate through one of several different vectors including emails, instant messaging systems, P2P networks, social networking websites, LANs, WANs, etc. Some more sophisticated viruses can even propagate through multiple vectors. These vectors can be classified into the two categories of social and technological networks as discussed above. Hence, computer viruses can be classified into three categories based on their propagation vectorssocial network (SN) based, technological network (TN) based, and composite network (CN) based. For example, MyDoom is primarily transmitted via email and P2P network and therefore is a SN-based virus. Unsuspecting computer users expose more personal information and are more vulnerable to SN-based viruses. The Blaster, as an example of TN-based viruses, starts from the local machine's IP address or a completely random address and attempts to infect sequential IP addresses. Nimda, a well-known multi-vector virus, spreads itself by different propagation methods including IP probing, email, network shares, etc. and therefore is a CN-based virus. 5 Prior research on computer viruses shows that network topology is crucial for virus propagation. In a computer virus incident, the topology of the victim network is the determinant factor of the propagation speed and destructive consequences. Towards this direction, researchers examine network topology to enhance computer security. For example, some research uses local network measures to explain virus propagation. Kephart and White incorporate average node degree into traditional epidemic models (Kephart and White 1991, Kephart and White 1993). Other studies consider specific topologies such as small-world network (Moore and Newman 2000) and scale-free network (May and Lloyd 2001, Pastor-Satorras and Vespignani 2001). Most extant work focuses on degree distributions and assumes certain distributions such as power-law distribution. However, most real world networks are not exactly scale-free (Balthrop et al. 2004). Few research incorporates network properties of individual nodes and examine network topology empirically. This paper empirically examines a large-scale organizational network which consists of both social network and technological network. We utilize a novel information retrieval technique to map nodes in the social network to nodes in the technological network to construct the composite network of the organization. We apply social network analysis to study the topologies of social, technological, and composite networks in an organization. We perform a comprehensive network analysis on these three networks and compare them both visually and quantitatively. Further, we statistically test the impact of the interplay of social and technological network on computer virus propagation using a susceptible-infective-recovered epidemic process. We find that computer viruses propagate faster but reach lower level of infection through technological network than through social network, and viruses propagate the fastest and reach the highest
doi:10.2139/ssrn.1304138 fatcat:yrnwckv2qrcrxezen5rhwawux4