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Node discovery problem for a social network, eprint arxiv.org/abs/0710
, 2007
"... This paper presents a practical heuristic algorithm to address a node discovery problem. The node discovery problem is to discover a clue on the person, who does not appear in the observed records, but is relevant functionally in affecting decisionmaking and behavior of an organization. We define t ..."
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This paper presents a practical heuristic algorithm to address a node discovery problem. The node discovery problem is to discover a clue on the person, who does not appear in the observed records, but is relevant functionally in affecting decisionmaking and behavior of an organization. We define two topological relevance of a node in a social network (global and local relevance). Association between the topological relevance and the functional relevance is studied with a few example networks in criminal organizations. We propose a heuristic algorithm to infer an invisible, functionally relevant person. Its performance (precision, recall, and F value) is demonstrated with a simulation experiment using a network derived from the WattsStrogatz (WS) model. 1 Node discovery problem The activity of an organization is often under influence from an invisible relevant person. The term, invisible, means that the influence is not seen directly by the method applied in the observation procedure. This phenomenon arises intentionally or unintentionally. Let us show 2 examples. 1. Criminal organization: A commander tries to conceal himself from leaving any traces in communication and meeting logs, which are the basic intelligence to the police. Otherwise, exposure and arrest of a relevant pilot would have been a fatal damage to the terrorist organization in the 9/11 attack. 2. Manufacturing company: A sales person happens to be a close friend of an expertise factory engineer through a common friend: a product designer.
Node discovery in a networked organization
, 803
"... Abstractâ€”In this paper, I present a method to solve a node discovery problem in a networked organization. Covert nodes refer to the nodes which are not observable directly. They affect social interactions, but do not appear in the surveillance logs which record the participants of the social interac ..."
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Abstractâ€”In this paper, I present a method to solve a node discovery problem in a networked organization. Covert nodes refer to the nodes which are not observable directly. They affect social interactions, but do not appear in the surveillance logs which record the participants of the social interactions. Discovering the covert nodes is defined as identifying the suspicious logs where the covert nodes would appear if the covert nodes became overt. A mathematical model is developed for the maximal likelihood estimation of the network behind the social interactions and for the identification of the suspicious logs. Precision, recall, and F measure characteristics are demonstrated with the dataset generated from a real organization and the computationally synthesized datasets. The performance is close to the theoretical limit for any covert nodes in the networks of any topologies and sizes if the ratio of the number of observation to the number of possible communication patterns is large.
Node Discovery Problem for a Social Network
"... A node discovery problem is defined as a problem in discovering a covert node within a social network. The covert node is a person who is not directly observable. The person transmits influence to neighbors and affects the resulting collaborative activities (e.g. meetings) within a social network, b ..."
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A node discovery problem is defined as a problem in discovering a covert node within a social network. The covert node is a person who is not directly observable. The person transmits influence to neighbors and affects the resulting collaborative activities (e.g. meetings) within a social network, but does not appear in any information reported by the intelligence. Throughout this study, the information comes from data that record the participants of collaborative activities. Discovery of the covert node refers to the retrieval of the data and the corresponding collaborative activities that result from the influence of the covert node. The nodes that appear commonly in the retrieved data are likely to neighbor the covert node. Two methods are presented for detecting covert nodes within a social network. A novel statistical inference method is discussed and compared with a conventional heuristic method (data crystallization). The statistical inference method employs the maximal likelihood estimation and outlier detection techniques. The performance of the methods is demonstrated with test datasets that are generated from computationally synthesized networks and from a real organization. Author: Dr. Yoshiharu Maeno is a founder management consultant and scientist at Social Design Group. He has developed mathematical methods to reveal the topological structure and to profile the information diffusion in social networks. Correspondence: Contact Yoshiharu Maeno at Sengoku 1638F, Bunkyoku, Tokyo 1120011, or email
Online Methods for . . . Localization
, 2008
"... Online techniques are presented for estimating the source and destination of a suspect transmission through a network based on the activation pattern of sensors placed on network components. A hierarchical Bayesian model is used to relate routing, tracking, and topological parameters. A controlled M ..."
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Online techniques are presented for estimating the source and destination of a suspect transmission through a network based on the activation pattern of sensors placed on network components. A hierarchical Bayesian model is used to relate routing, tracking, and topological parameters. A controlled Markovian routing model is used in conjunction with a recursive EM algorithm to derive adaptive routing and tracking parameter estimates. Previously developed semidefinite programming methods are used to account for any prior topological information through Monte Carlo estimates of the topology parameters. Convergence of the routing and tracking parameter estimates is proven and it is shown that their asymptotic estimates are fixed points of an exact EM algorithm. Approximate methods based on permutation clustering are presented to reduce the complexity of sums that arise in the estimator formulas. A multiarmed bandit approach to the design problem of online probe scheduling is also presented. Finally, the effectiveness of the new methods is illustrated through a variety of tracking simulations inspired by real world scenarios and involving real Internet data. Speedy performance and good accuracy are observed.
Profiling of a network behind an infectious disease outbreak
, 2009
"... I describe a method to estimate a social network topology and diffusion parameters from the time sequence data of an infectious disease outbreak. The method is applicable to a stochastic diffusion process in a metapopulation and ..."
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I describe a method to estimate a social network topology and diffusion parameters from the time sequence data of an infectious disease outbreak. The method is applicable to a stochastic diffusion process in a metapopulation and
1 A SURVEY OF COMPUTATIONAL APPROACHES TO RECONSTRUCT AND PARTITION BIOLOGICAL NETWORKS
"... The above quote by Theodor Holm Nelson, the pioneer of information technology, states a deep interconnectedness among the myriad topics of this world. The biological systems are no exceptions, which comprise of a complex web of biomolecular interactions and regulation processes. In particular, the f ..."
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The above quote by Theodor Holm Nelson, the pioneer of information technology, states a deep interconnectedness among the myriad topics of this world. The biological systems are no exceptions, which comprise of a complex web of biomolecular interactions and regulation processes. In particular, the field of computational