text: The investigation of the terrorist attack is a time-critical task. The investigators have a limited time window to diagnose the organizational background of the terrorists, to run down and arrest the wire-pullers, and to take an action to prevent or eradicate the terrorist attack. The intuitive interface to visualize the intelligence data set stimulates the investigators’ experience and knowledge, and aids them in decision-making for an immediately effective action. This paper presents a computational method to analyze the intelligence data set on the collective actions of the perpetrators of the attack, and to visualize it into the form of a social network diagram which predicts the positions where the wire-pullers conceals themselves.
  @cite_18: We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems.
  @cite_7: Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains.
  @cite_9: This paper addresses a method to analyse the covert social network foundation hidden behind the terrorism disaster. It is to solve a node discovery problem, which means to discover a node, which functions relevantly in a social network, but escaped from monitoring on the presence and mutual relationship of nodes. The method aims at integrating the expert investigator's prior understanding, insight on the terrorists' social network nature derived from the complex graph theory and computational data processing. The social network responsible for the 9 11 attack in 2001 is used to execute simulation experiment to evaluate the performance of the method.
  @cite_5: Experts of chance discovery have recognized a new class of problems where the previous methods fail to visualize a latent structure behind observation. There are invisible events that play an important role in the dynamics of visible events. An invisible leader in a communication network is a typical example. Such an event is named a dark event. A novel technique has been proposed to understand a dark event and to extend the process of chance discovery. This paper presents a new method named "human-computer interactive annealing" for revealing latent structures along with the algorithm for discovering dark events. Demonstration using test data generated from a scale-free network shows that the precision regarding the algorithm ranges from 80 to 90 . An experiment on discovering an invisible leader under an online collective decision-making circumstance is successful
  @cite_10: This paper introduces the concept of chance discovery, i.e. discovery of an event significant for decision making. Then, this paper also presents a current research project on data crystallization, which is an extension of chance discovery. The need for data crystallization is that only the observable part of the real world can be stored in data. For such scattered, i.e. incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable ones. This is realized with a tool which inserts dummy items, corresponding to unobservable but significant events, to the given data on past events. The existence of these unobservable events and their relations with other events are visualized with KeyGraph, showing events by nodes and their relations by links, on the data with inserted dummy items. This visualization is iterated with gradually increasing the number of links in the graph. This process is similar to the crystallization of snow with gradual decrease in the air temperature. For tuning the granularity level of structure to be visualized, this tool is integrated with human's process of chance discovery. This basic method is expected to be applicable for various real world domains where chance-discovery methods have been applied.
summary: On the other hand, the node discovery predicts the existence of an unknown node around the known nodes from the information on the collective behavior of the network. Related works in the node discovery is, however, limited. Heuristic method for node discovery is proposed in @cite_5 , @cite_10 . The method is applied to analyze the covert social network foundation behind the terrorism disasters @cite_9 . Learning techniques of latent variables can be employed, once the presence of a node is known. @cite_18 studied learning of a structure of a linear latent variable graph. @cite_7 studied learning of a structure of a dynamic probabilistic network. But, while the accuracy of the heuristic method is limited, these principled analytic approaches in learning are not practical to handle real human relationship and communication observed in a social network, where much complexity appears. The complexity includes bi-directional and cyclic influence among many observed and latent nodes. We need an efficient and accurate method to solve the node discovery problem.