Information Spreading Forensics via Sequential Dependent Snapshots

Mining the characteristics of information spreading in networks is crucial in communication studies, network security management, epidemic investigations, etc. Previous works are restrictive because they mainly focused on the information source detection using either a single observation, or multiple but independent observations of the underlying network while assuming a homogeneous information spreading rate. We conduct a theoretical and experimental study on information spreading, and propose a new and novel estimation framework to estimate 1) information spreading rates, 2) start time of the information source, and 3) the location of information source by utilizing multiple sequential and dependent snapshots where information can spread at heterogeneous rates. Our framework generalizes the current state-of-the-art rumor centrality [1] and the union rumor centrality [2]. Furthermore, we allow heterogeneous information spreading rates at different branches of a network. Our framework provides conditional maximum likelihood estimators for the above three metrics and is more accurate than rumor centrality and Jordan center in both synthetic networks and real-world networks. Applying our framework to the Twitter's retweet networks, we can accurately determine who made the initial tweet and at what time the tweet was sent. Furthermore, we also validate that the rates of information spreading are indeed heterogeneous among different parts of a retweet network.