The Dynamics of Web-Based Community Safety Groups: Lessons Learned from the Nation of Neighbors [Social Sciences]

Sopan, Awalin; Rey, PJ; Shneiderman, Ben

doi:10.1109/msp.2013.2276513

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Nodes and Links: Because two graphs may have similar gragnostic features but be significantly different in size, the number of vertices and edges is useful for discriminating graphs or finding graphs of a particular size. Gragnostics uses the name "nodes" for vertices, and the name "links" for edges because these terms are not based on graph theory, and they are among the most commonly used words in present-day English 1 . This makes the names more likely to be understood by analysts and users without a background in graph theory.…”

Section: A Featuresmentioning

confidence: 99%

“…Comparing graphs is a common task-e.g. finding successful community characteristics [1], or predicting graph layout quality [2]. Algorithms for this task are often slow, uninterpretable, or have restrictions, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this paper are (1) two new graph-level features for measuring how tree-like and how line-like a graph is; (2) an evaluation of gragnostics, a set of 10 graph-level features for machine learning classifiers and rank-by-feature visualization tools; (3) a rank-by-feature approach for visual analytics on subgraphs of a larger graph; and (4) a replication of other visualization tool design research that confirms their findings and the utility of subgraph clustering.…”

Section: Introductionmentioning

confidence: 99%

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Gragnostics: Fast, Interpretable Features for Comparing Graphs

Gove¹

2019

2019 23rd International Conference Information Visualisation (IV)

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Many analytical tasks, such as social network analysis, depend on comparing graphs. Existing methods are slow, or can be difficult to understand. To address these challenges, this paper proposes gragnostics, a set of 10 fast, laypersonunderstandable graph-level features. Each can be computed in linear time. To evaluate the ability of these features to discriminate different topologies and types of graphs, this paper compares a machine learning classifier using gragnostics to alternative classifiers, and the evaluation finds that the gragnostics classifier achieves higher performance. To evaluate gragnostics' utility in interactive visualization tools, this paper presents Chiron, a graph visualization tool that enables users to explore the subgraphs of a larger graph. Example usage scenarios of Chiron demonstrate that using gragnostics in a rank-by-feature framework can be effective for finding interesting subgraphs.

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Section: A Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gragnostics: Fast, Interpretable Features for Comparing Graphs

Gove¹

2019

2019 23rd International Conference Information Visualisation (IV)

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“…A vast number of studies have been conducted to analyze inherent properties and behaviors found in cities. For instance, multiple metrics have been adopted to explain their structural conditions [3], their intense traffic of vehicles [4], and the emergence of collective behavior [5]. In other studies, the authors centered on the geometrical perspective of the network [6], and on the elements positioning [7,8].…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Distance-Based Tool-Set to Track Inconsistent Urban Structures Through Complex-Networks

Spadon

Machado

Eler

et al. 2018

Lecture Notes in Computer Science

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Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex networks, detecting nodes that lack access from/to points of interest in a given city. Furthermore, we introduce a greedy algorithm that is able to recommend improvements to the structure of a city by suggesting where points of interest are to be placed. We contribute to a thorough process to deal with complex networks, including mathematical modeling and algorithmic innovation. The set of our contributions introduces a systematic manner to treat a recurrent problem of broad interest in cities.

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“…Other authors [16,17] covered points related to the optimization of transportation, mainly with the allocation of resources in street networks. Notwithstanding, to solve issues related to the urban scenario, several authors have been considering data derived from the structural conditions of cities [18], the intense traffic of vehicles [19] and collective behavior of their citizens [20,21]. This is the case of Viana and Fontoura [22], who explored the impact of fast long-range connections to improve the traffic between two points.…”

Section: Rationalementioning

confidence: 99%

Characterization of mobility patterns and collective behavior through the analytical processing of real-world complex networks.

Spadon¹

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Dedicated to my grandfather Antônio Spadon and to the loving memory of my grandmother Marileide Pardo Spadon. I would like to express my kindly gratitude to my advisor, Ph.D. Jose Fernando Rodrigues Junior, who wisely and patiently guided my dissertation work, providing plenty of academic opportunities by teaching me how to research. I am also grateful to my research group colleagues (GBdI) for the good times that we spent together these last years, which was of significant importance to motivate my work and myself. I kindly thank my bachelor's professors from Sao Paulo State University (UNESP) that provided me with all the means to go further during my undergraduate course, making possible for me to be accepted in a post-graduation program. Furthermore, my family and girlfriend had a fundamental importance in this work by always supporting me and believing in my dreams, no matter the difficulties that I faced to achieve them; they always stood by my side. My sincere thanks to my grandparents, who always encourage me to go further, despite not understanding the academic world. Moreover, my recognition to my hometown friends, the ones that granted me with incredible life experiences through remarkable years of friendship. Finally, yet importantly, I would like to thank CAPES that fully financed my master's degree. "Hey you, don't tell me there's no hope at all. Together we stand, divided we fall."

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The Dynamics of Web-Based Community Safety Groups: Lessons Learned from the Nation of Neighbors [Social Sciences]

Cited by 6 publications

References 7 publications

Gragnostics: Fast, Interpretable Features for Comparing Graphs

Gragnostics: Fast, Interpretable Features for Comparing Graphs

A Distance-Based Tool-Set to Track Inconsistent Urban Structures Through Complex-Networks

Characterization of mobility patterns and collective behavior through the analytical processing of real-world complex networks.

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