Many problems in information retrieval and related fields depend on a reliable measure of the distance or similarity between objects that, most frequently, are represented
as vectors. This paper considers vectors of bits. Such data structures implement entities as diverse as bitmaps that indicate the occurrences of terms and bitstrings indicating the presence
of edges in images. For such applications, a popular distance measure is the Hamming distance. The value of the Hamming distance for information retrieval applications is limited by the
fact that it counts only exact matches, whereas in information retrieval, corresponding bits that are close by can still be considered to be almost identical. We define a "Generalized
Hamming distance" that extends the Hamming concept to give partial credit for near misses, and suggest a dynamic programming algorithm that permits it to be computed efficiently.
We envision many uses for such a measure. In this paper we define and prove some basic properties of the :Generalized Hamming distance," and illustrate its use in the area of object
recognition. We evaluate our implementation in a series of experiments, using autonomous robots to test the measure's effectiveness in relating similar bitstrings.
(Paper) BalloonNet: A Deploying Method for a Three-Dimensional Wireless Netwo...Naoki Shibata
Aiming at fast establishment of a wireless network around a multi-level building in a disaster area, we propose an efficient method to determine the locations of network nodes in the air. Nodes are attached to balloons outside a building and deployed in the air so that the network can be accessed from anywhere in the building. In this paper, we introduce an original radio propagation model for predicting path loss from an outdoor position to a position inside a building. In order to address the three-dimensional deployment problem, the proposed method optimizes an objective function for satisfying two goals: (1) guarantee the coverage: the target space needs to be covered by over a certain percentage by wireless network nodes, (2) minimize the number of network nodes. For solving this problem, we propose an algorithm based on a genetic algorithm. To evaluate the proposed method, we compared our method with three benchmark methods, and the results show that the proposed method requires fewer nodes than other methods.
This talk is an overview of several species of agent-based models. The goal is to identify common aspects, and to frame questions about how statistical inference on such models might be done.
Articulo Científico "Energy Flow Algorithm for the improvement of the Energy ...CARMEN IGLESIAS
This note wroten by Carmen Iglesias Escudero explains the aplication of the Energy Flow algorithm in order to improve the energy resolution of the jets reconstructe by the fast simulation package of ATLAS namely Atlfast. The results are been calculated for different values of the cone, 0,4 and 0,7 and different range of Et of the generated QCD jets, in order to compare the behaviour of the algorithm whith the variation of these parameters. We can conclude, that considering the region of Et where the momentum resolution of the inner detector is better than the energy resolution of the hadronic calorimeter, below 140 GeV, the use of the Energy Flow Method give us an improvement in the energy resolution of the jet around 45-40% and 35-30% for R=0,7.
Many problems in information retrieval and related fields depend on a reliable measure of the distance or similarity between objects that, most frequently, are represented
as vectors. This paper considers vectors of bits. Such data structures implement entities as diverse as bitmaps that indicate the occurrences of terms and bitstrings indicating the presence
of edges in images. For such applications, a popular distance measure is the Hamming distance. The value of the Hamming distance for information retrieval applications is limited by the
fact that it counts only exact matches, whereas in information retrieval, corresponding bits that are close by can still be considered to be almost identical. We define a "Generalized
Hamming distance" that extends the Hamming concept to give partial credit for near misses, and suggest a dynamic programming algorithm that permits it to be computed efficiently.
We envision many uses for such a measure. In this paper we define and prove some basic properties of the :Generalized Hamming distance," and illustrate its use in the area of object
recognition. We evaluate our implementation in a series of experiments, using autonomous robots to test the measure's effectiveness in relating similar bitstrings.
(Paper) BalloonNet: A Deploying Method for a Three-Dimensional Wireless Netwo...Naoki Shibata
Aiming at fast establishment of a wireless network around a multi-level building in a disaster area, we propose an efficient method to determine the locations of network nodes in the air. Nodes are attached to balloons outside a building and deployed in the air so that the network can be accessed from anywhere in the building. In this paper, we introduce an original radio propagation model for predicting path loss from an outdoor position to a position inside a building. In order to address the three-dimensional deployment problem, the proposed method optimizes an objective function for satisfying two goals: (1) guarantee the coverage: the target space needs to be covered by over a certain percentage by wireless network nodes, (2) minimize the number of network nodes. For solving this problem, we propose an algorithm based on a genetic algorithm. To evaluate the proposed method, we compared our method with three benchmark methods, and the results show that the proposed method requires fewer nodes than other methods.
This talk is an overview of several species of agent-based models. The goal is to identify common aspects, and to frame questions about how statistical inference on such models might be done.
Articulo Científico "Energy Flow Algorithm for the improvement of the Energy ...CARMEN IGLESIAS
This note wroten by Carmen Iglesias Escudero explains the aplication of the Energy Flow algorithm in order to improve the energy resolution of the jets reconstructe by the fast simulation package of ATLAS namely Atlfast. The results are been calculated for different values of the cone, 0,4 and 0,7 and different range of Et of the generated QCD jets, in order to compare the behaviour of the algorithm whith the variation of these parameters. We can conclude, that considering the region of Et where the momentum resolution of the inner detector is better than the energy resolution of the hadronic calorimeter, below 140 GeV, the use of the Energy Flow Method give us an improvement in the energy resolution of the jet around 45-40% and 35-30% for R=0,7.
This document provides an overview of exponential random graph models (ERGMs) for statistically modeling social networks. It discusses the goals of using ERGMs, which are to understand structural features of networks, test hypotheses about network formation processes, and link macro network structures to micro behaviors. Example model terms that can be used in ERGMs are described, ranging from simple models with just edges to more complex models incorporating triangles, degree distributions, and homophily. The document outlines the challenges of estimating ERGM parameters using maximum likelihood due to the normalizing constant, and notes that simulation-based approximations are typically used.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the relevant population boundaries and sampling approaches. For surveys specifically, it covers informed consent, name generator questions to identify social ties, response formats, and balancing depth of network detail collected versus sample size. The key challenges are defining the theoretical population of interest, collecting a sufficiently large and representative network sample, and designing survey questions that accurately capture social ties within time and resource constraints.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
12 Network Experiments and Interventions: Studying Information Diffusion and ...dnac
This document summarizes research on studying information diffusion and collective action through network experiments and interventions. The research aims to identify optimal strategies for information dissemination for public policy by comparing the effectiveness of different dissemination methods, including using phone/IVR, government representatives, and social network seeds. It also examines how an individual's decision to participate is influenced by information and participation within their social network, and whether there are threshold or free-riding effects. The proposed experiments will randomize information dissemination methods and incentives for individuals and networks to participate in community activities across villages in India. Network and individual participation data will be collected through surveys to analyze the impact of social networks and information on collective action.
13 An Introduction to Stochastic Actor-Oriented Models (aka SIENA)dnac
This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
The document discusses different types of network experiments and interventions. It describes (1) assigning roommates randomly to manipulate networks and assess peer effects, (2) using natural experiments to manipulate exposure over existing networks, and (3) interventions that use networks to affect change. Specifically, it covers exogenous network experiments that randomly assign relationships, issues with experimental assignment, and four types of interventions: targeting individuals, segmentation, induction, and alteration.
This document provides an overview of community detection in networks. It begins with an introduction to the concept of communities and their usefulness in network analysis. It then discusses two main approaches to calculating communities - descriptive methods like modularity, and generative methods like stochastic block models. The document notes that community detection is an active area of research, with opportunities to extend current methods. It provides several examples of community detection applications and acknowledges contributions from other researchers in the field.
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
The document discusses network diffusion and peer influence. It covers compartmental models of diffusion, how network structure affects diffusion through factors like distance, clustering, and highly connected nodes. Simulation studies show networks with shorter path distances, more independent paths between nodes, and higher clustering coefficients diffuse ideas and behaviors more quickly. The regression analysis finds these network structural characteristics strongly predict a network's relative diffusion ratio compared to random networks.
Random graphs and graph randomization procedures can be used for inference, simulation, and measuring networks. [1] Erdos random graphs are the simplest random graphs where each edge has an equal probability of being present. [2] More complex random graph models can be generated that preserve properties like degree distributions or mixing patterns observed in real networks. [3] Analyzing the distribution of triadic subgraphs (motifs) in a network compared to random graphs can test hypothesized mechanisms of network formation.
06 Network Study Design: Ethical Considerations and Safeguardsdnac
This document outlines ethical considerations and safeguards for social network study design. It discusses principles from the Belmont Report including respect for persons, beneficence, and justice. Key risks in social network research are deductive disclosure, outing people, and legal or privacy risks from relational data. Mitigation strategies include data agreements, restricting access to identifying data, training researchers, and communicating clearly with IRBs. The document emphasizes that social network studies require safeguarding participant and alter privacy.
09 Respondent Driven Sampling and Network Sampling with Memorydnac
RDS and network sampling methods aim to sample hidden populations for which traditional sampling frames do not exist. The document discusses issues with sampling hidden populations and evaluates Respondent Driven Sampling (RDS) and a new method called Network Sampling with Memory (NSM). It finds that RDS estimates can be biased when its assumptions are violated. A new data collection method called Inverse Preferential RDS (IP-RDS) and the NSM method show promise in improving estimation through modifications to the sampling process and collection of network data. Field testing is still needed to validate these innovative approaches.
This document provides an overview of ego network analysis. It defines ego networks as consisting of a focal individual (ego) and the people they are connected to (alters). Various measures of ego network composition, structure, and properties can be analyzed, such as size, density, and homophily. These measures provide insight into an individual's social support and influence, and can be used to study health-related questions by examining the characteristics and behaviors present in one's social network. Ego network data is relatively easy to collect and can offer information about both individuals and inferred properties of broader social networks.
This document provides an overview of exponential random graph models (ERGMs) for statistically modeling social networks. It discusses the goals of using ERGMs, which are to understand structural features of networks, test hypotheses about network formation processes, and link macro network structures to micro behaviors. Example model terms that can be used in ERGMs are described, ranging from simple models with just edges to more complex models incorporating triangles, degree distributions, and homophily. The document outlines the challenges of estimating ERGM parameters using maximum likelihood due to the normalizing constant, and notes that simulation-based approximations are typically used.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the relevant population boundaries and sampling approaches. For surveys specifically, it covers informed consent, name generator questions to identify social ties, response formats, and balancing depth of network detail collected versus sample size. The key challenges are defining the theoretical population of interest, collecting a sufficiently large and representative network sample, and designing survey questions that accurately capture social ties within time and resource constraints.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
12 Network Experiments and Interventions: Studying Information Diffusion and ...dnac
This document summarizes research on studying information diffusion and collective action through network experiments and interventions. The research aims to identify optimal strategies for information dissemination for public policy by comparing the effectiveness of different dissemination methods, including using phone/IVR, government representatives, and social network seeds. It also examines how an individual's decision to participate is influenced by information and participation within their social network, and whether there are threshold or free-riding effects. The proposed experiments will randomize information dissemination methods and incentives for individuals and networks to participate in community activities across villages in India. Network and individual participation data will be collected through surveys to analyze the impact of social networks and information on collective action.
13 An Introduction to Stochastic Actor-Oriented Models (aka SIENA)dnac
This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
The document discusses different types of network experiments and interventions. It describes (1) assigning roommates randomly to manipulate networks and assess peer effects, (2) using natural experiments to manipulate exposure over existing networks, and (3) interventions that use networks to affect change. Specifically, it covers exogenous network experiments that randomly assign relationships, issues with experimental assignment, and four types of interventions: targeting individuals, segmentation, induction, and alteration.
This document provides an overview of community detection in networks. It begins with an introduction to the concept of communities and their usefulness in network analysis. It then discusses two main approaches to calculating communities - descriptive methods like modularity, and generative methods like stochastic block models. The document notes that community detection is an active area of research, with opportunities to extend current methods. It provides several examples of community detection applications and acknowledges contributions from other researchers in the field.
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
The document discusses network diffusion and peer influence. It covers compartmental models of diffusion, how network structure affects diffusion through factors like distance, clustering, and highly connected nodes. Simulation studies show networks with shorter path distances, more independent paths between nodes, and higher clustering coefficients diffuse ideas and behaviors more quickly. The regression analysis finds these network structural characteristics strongly predict a network's relative diffusion ratio compared to random networks.
Random graphs and graph randomization procedures can be used for inference, simulation, and measuring networks. [1] Erdos random graphs are the simplest random graphs where each edge has an equal probability of being present. [2] More complex random graph models can be generated that preserve properties like degree distributions or mixing patterns observed in real networks. [3] Analyzing the distribution of triadic subgraphs (motifs) in a network compared to random graphs can test hypothesized mechanisms of network formation.
06 Network Study Design: Ethical Considerations and Safeguardsdnac
This document outlines ethical considerations and safeguards for social network study design. It discusses principles from the Belmont Report including respect for persons, beneficence, and justice. Key risks in social network research are deductive disclosure, outing people, and legal or privacy risks from relational data. Mitigation strategies include data agreements, restricting access to identifying data, training researchers, and communicating clearly with IRBs. The document emphasizes that social network studies require safeguarding participant and alter privacy.
09 Respondent Driven Sampling and Network Sampling with Memorydnac
RDS and network sampling methods aim to sample hidden populations for which traditional sampling frames do not exist. The document discusses issues with sampling hidden populations and evaluates Respondent Driven Sampling (RDS) and a new method called Network Sampling with Memory (NSM). It finds that RDS estimates can be biased when its assumptions are violated. A new data collection method called Inverse Preferential RDS (IP-RDS) and the NSM method show promise in improving estimation through modifications to the sampling process and collection of network data. Field testing is still needed to validate these innovative approaches.
This document provides an overview of ego network analysis. It defines ego networks as consisting of a focal individual (ego) and the people they are connected to (alters). Various measures of ego network composition, structure, and properties can be analyzed, such as size, density, and homophily. These measures provide insight into an individual's social support and influence, and can be used to study health-related questions by examining the characteristics and behaviors present in one's social network. Ego network data is relatively easy to collect and can offer information about both individuals and inferred properties of broader social networks.
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