180 research outputs found
Deeper-GXX: Deepening Arbitrary GNNs
Shallow GNNs tend to have sub-optimal performance dealing with large-scale
graphs or graphs with missing features. Therefore, it is necessary to increase
the depth (i.e., the number of layers) of GNNs to capture more latent knowledge
of the input data. On the other hand, including more layers in GNNs typically
decreases their performance due to, e.g., vanishing gradient and oversmoothing.
Existing methods (e.g., PairNorm and DropEdge) mainly focus on addressing
oversmoothing, but they suffer from some drawbacks such as requiring
hard-to-acquire knowledge or having large training randomness. In addition,
these methods simply incorporate ResNet to address vanishing gradient. They
ignore an important fact: by stacking more and more layers with ResNet
architecture, the information collected from faraway neighbors becomes
dominant, compared with the information collected from the 1-hop and 2-hop
neighbors, thus resulting in severe performance degradation. In this paper, we
first go deep into the architecture of ResNet and analyze why ResNet is not
best suited for deeper GNNs. Then we propose a new residual architecture to
attenuate the negative impact caused by ResNet. To address the drawbacks of
these existing methods, we introduce the Topology-guided Graph Contrastive Loss
named TGCL. It utilizes node topological information and pulls the connected
node pairs closer via contrastive learning regularization to obtain
discriminative node representations. Combining the new residual architecture
with TGCL, an end-to-end framework named Deeper-GXX is proposed towards deeper
GNNs. The extensive experiments on real-world data sets demonstrate the
effectiveness and efficiency of Deeper-GXX compared with state-of-the-art
baselines
GeoLinter: A Linting Framework for Choropleth Maps
Visualization linting is a proven effective tool in assisting users to follow
established visualization guidelines. Despite its success, visualization
linting for choropleth maps, one of the most popular visualizations on the
internet, has yet to be investigated. In this paper, we present GeoLinter, a
linting framework for choropleth maps that assists in creating accurate and
robust maps. Based on a set of design guidelines and metrics drawing upon a
collection of best practices from the cartographic literature, GeoLinter
detects potentially suboptimal design decisions and provides further
recommendations on design improvement with explanations at each step of the
design process. We perform a validation study to evaluate the proposed
framework's functionality with respect to identifying and fixing errors and
apply its results to improve the robustness of GeoLinter. Finally, we
demonstrate the effectiveness of the GeoLinter - validated through empirical
studies - by applying it to a series of case studies using real-world datasets.Comment: to appear in IEEE Transactions on Visualization and Computer Graphic
Abstract Feature Space Representation for Volumetric Transfer Function Exploration
The application of n-dimensional transfer functions for feature segmentation has become increasingly popular in volume rendering. Recent work has focused on the utilization of higher order dimensional transfer functions incorporating spatial dimensions (x,y, and z) along with traditional feature space dimensions (value and value gradient). However, as the dimensionality increases, it becomes exceedingly difficult to abstract the transfer function into an intuitive and interactive workspace. In this work we focus on populating the traditional two-dimensional histogram with a set of derived metrics from the spatial (x, y and z) and feature space (value, value gradient, etc.) domain to create a set of abstract feature space transfer function domains. Current two-dimensional transfer function widgets typically consist of a two-dimensional histogram where each entry in the histogram represents the number of voxels that maps to that entry. In the case of an abstract transfer function design, the amount of spatial variance at that histogram
coordinate is mapped instead, thereby relating additional information about the data abstraction in the projected space. Finally, a non-parametric kernel density estimation approach for feature space clustering is applied in the abstracted space, and the resultant transfer functions are discussed with respect to the space abstraction
Sky View Factor footprints for urban climate modeling
Urban morphology is an important multidimensional variable to consider in climate modeling and observations, because it significantly drives the local and micro-scale climatic variability in cities. Urban form can be described through urban canopy parameters (UCPs) that resolve the spatial heterogeneity of cities by specifying the 3-dimensional geometry, arrangement, and materials of urban features. The sky view factor (SVF) is a dimension-reduced UCP capturing 3-dimensional form through horizon limitation fractions. SVF has become a popular metric to parameterize urban morphology, but current approaches are difficult to scale up to global coverage. This study introduces a Big-Data approach to calculate SVFs for urban areas from Google Street View (GSV). 90-degree field-of-view GSV photos are retrieved and converted into hemispherical views through equiangular projection. The fisheyes are segmented into sky and non-sky pixels using image processing, and the SVF is calculated using an annulus method. Results are compared to SVFs retrieved from GSV images segmented using deep learning. SVF footprints are presented for urban areas around the world tallying 15,938,172 GSV locations. Two use cases are introduced: (1) an evaluation of a Google Earth Engine classified Local Climate Zone map for Singapore; (2) hourly sun duration maps for New York and San Francisco
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
In graph machine learning, data collection, sharing, and analysis often
involve multiple parties, each of which may require varying levels of data
security and privacy. To this end, preserving privacy is of great importance in
protecting sensitive information. In the era of big data, the relationships
among data entities have become unprecedentedly complex, and more applications
utilize advanced data structures (i.e., graphs) that can support network
structures and relevant attribute information. To date, many graph-based AI
models have been proposed (e.g., graph neural networks) for various domain
tasks, like computer vision and natural language processing. In this paper, we
focus on reviewing privacy-preserving techniques of graph machine learning. We
systematically review related works from the data to the computational aspects.
We first review methods for generating privacy-preserving graph data. Then we
describe methods for transmitting privacy-preserved information (e.g., graph
model parameters) to realize the optimization-based computation when data
sharing among multiple parties is risky or impossible. In addition to
discussing relevant theoretical methodology and software tools, we also discuss
current challenges and highlight several possible future research opportunities
for privacy-preserving graph machine learning. Finally, we envision a unified
and comprehensive secure graph machine learning system.Comment: Accepted by SIGKDD Explorations 2023, Volume 25, Issue
Deceptive Fairness Attacks on Graphs via Meta Learning
We study deceptive fairness attacks on graphs to answer the following
question: How can we achieve poisoning attacks on a graph learning model to
exacerbate the bias deceptively? We answer this question via a bi-level
optimization problem and propose a meta learning-based framework named FATE.
FATE is broadly applicable with respect to various fairness definitions and
graph learning models, as well as arbitrary choices of manipulation operations.
We further instantiate FATE to attack statistical parity and individual
fairness on graph neural networks. We conduct extensive experimental
evaluations on real-world datasets in the task of semi-supervised node
classification. The experimental results demonstrate that FATE could amplify
the bias of graph neural networks with or without fairness consideration while
maintaining the utility on the downstream task. We hope this paper provides
insights into the adversarial robustness of fair graph learning and can shed
light on designing robust and fair graph learning in future studies.Comment: 23 pages, 11 table
MolSieve: A Progressive Visual Analytics System for Molecular Dynamics Simulations
Molecular Dynamics (MD) simulations are ubiquitous in cutting-edge
physio-chemical research. They provide critical insights into how a physical
system evolves over time given a model of interatomic interactions.
Understanding a system's evolution is key to selecting the best candidates for
new drugs, materials for manufacturing, and countless other practical
applications. With today's technology, these simulations can encompass millions
of unit transitions between discrete molecular structures, spanning up to
several milliseconds of real time. Attempting to perform a brute-force analysis
with data-sets of this size is not only computationally impractical, but would
not shed light on the physically-relevant features of the data. Moreover, there
is a need to analyze simulation ensembles in order to compare similar processes
in differing environments. These problems call for an approach that is
analytically transparent, computationally efficient, and flexible enough to
handle the variety found in materials based research. In order to address these
problems, we introduce MolSieve, a progressive visual analytics system that
enables the comparison of multiple long-duration simulations. Using MolSieve,
analysts are able to quickly identify and compare regions of interest within
immense simulations through its combination of control charts, data-reduction
techniques, and highly informative visual components. A simple programming
interface is provided which allows experts to fit MolSieve to their needs. To
demonstrate the efficacy of our approach, we present two case studies of
MolSieve and report on findings from domain collaborators.Comment: Updated references to GPCC
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