Identification of Individual Infection Over Networks With Limit Observation: Random vs. Epidemic?

Abstract

Popular information or dangerous viruses have recently been observed to spread rapidly through a highly connected network structure. For example, malicious viruses and rumors are spreading rapidly through online social network platforms over the Internet and diseases with high transmission power are rapidly spreading through human contact. Apart from these, some infections may occur individually regardless of the effect of the network such as computer failure. In the context of these infections coexisting, it is one of crucial problem to distinguish whether the infection with externally similar symptoms is caused by a cascade or by itself because if the infection is the cascade that is spreading through the network, it is necessary to stop this spread as soon as possible. In this paper, we study this classification problem to determine whether it is infected from a cascade or randomly when the infection snapshot is partially given. We propose two approaches for the problem (i) Neighbor-based approach as the use of local infection status information and (ii) Source-based approach as the global infection status information. The first one is that we just count the number of connected infection paths from L-hop distance to an infected node by using some criteria whereas, the second one is that we use the information of the location of cascade sources to infer the infection cause of each infected node by computing the infection probability from the sources. We perform various simulations to obtain the classification performance of the two proposed algorithms. As a result, the method of estimating a global cascade source shows better performance than that of the former one, which uses only the infection information of local neighboring nodes if the sampling rate of cascade infections is sufficient.

Inferring the Hidden Cascade Infection over Erdös-Rényi (ER) Random Graph

“…He et al [9] proposed an algorithm that effectively recovers a graph with partial cascade samples by considering a generative model, which is referred to as a Multi-Cascaded Model (MCM). In addition, Choi et al [10] investigated the classification of causes of infection (random infection versus cascade infection) in a network under a partial observation of the infection status. To do this, they also considered the hidden cascade recovery approach in their proposed iterative classification algorithm.…”