
## 5 Related Works

Existing terrorist or criminal social networks are studied empirically. (Batallas, 2006) 
applied centrality (Freeman, 1979) and brokerage (Cusumano, 2000) to analyze an aircraft engine development project, and suggested relevance of an information leader team, which could be either a bottleneck or an innovation diffuser. (Keila, 2006) applied factor analysis to study email exchange in Enron, which ended in bankruptcy due to the institutionalized accounting fraud. (Klerks, 2002) points out that criminal organizations tend to be strings of inter-linked small groups that lack a central leader, but to coordinate their activities along logistic trails and through bonds of friends, and that hypothesis can be built by paying attention to remarkable white spots and hard-to-fill positions in a network. (Krebs, 2002) investigates the 9/11 terrorist network, and reveals that the relevance of conspirators who reduce the distance between hijackers and enhance communication efficiently. (Morselli, 2007) investigates Kreb's network from the viewpoint of efficiency and security trade-off, and suggests that more security-oriented structure arises from longer time-to-task of the terrorists' objectives, and that conspirators improve communication efficiency, preserving hijackers' small visibility and exposure. 

Complex network, graph theory, and learning help us get an insight on the dynamics of a social network, in addition to summarizing and visualizing a network (Shen, 2007), and analyzing a cognitive network (Krackhardt, 1987). Scale-free networks (Barabasi, 1999) and small worlds (Watts, 1998) present us much insight on the structure and evolution of a social network: scientists' collaboration, actors in movies etc. A power law in the nodal degree distribution governs the scale-free network. (Fenner, 2007) proposes an exponential cutoff mechanism to modify the power law. Error attack tolerance (Albert, 2000) and search efficiency (Adamic, 2001) are of particular interest for practical applications. 

Link discovery is applied to predict collaboration between scientists from the published co-authorship (Liben-Nowell, 2004). (Adamic, 2003) proposes a technique to infer friends and neighbors from the information available on the web. (Singh, 2004) applied a hidden Markov model and a Bayesian network to predict the behavior of terrorists. Learning of a Bayesian network is extended to study the probabilistic nature of latent variables. (Silva, 2006) studied learning of a structure of a linear latent variable graph. (Friedman, 1998) studied learning of a structure of a dynamic probabilistic network. The principled analytic approach often suffers from complexity problem. The complexity includes bi-directional and cyclic influence among the many observed and latent nodes (beyond a triad: 1 latent node influencing 2 observed nodes). 

## 6 Concluding Remark

In this paper, we demonstrate the proposed method to analyze the covert social network foundation hidden behind the terrorism disaster. The method integrates the expert investigator's prior understanding, insight on the terrorists' social network nature derived from the complex graph theory, and computational data processing. It is effective to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of nodes. Precision, recall, and F value characteristics of the algorithm are evaluated in the simulation experiment using the social network responsible for the 9/11 attack in 2001. 