43,079 research outputs found
Adaptive aggregation on graphs
We generalize some of the functional (hyper-circle) a posteriori estimates
from finite element settings to general graphs or Hilbert space settings. We
provide several theoretical results in regard to the generalized a posteriori
error estimators. We use these estimates to construct aggregation based coarse
spaces for graph Laplacians. The estimator is used to assess the quality of an
aggregation adaptively. Furthermore, a reshaping algorithm based is tested on
several numerical examples.Comment: 17 page
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
We propose a dynamic neighborhood aggregation (DNA) procedure guided by
(multi-head) attention for representation learning on graphs. In contrast to
current graph neural networks which follow a simple neighborhood aggregation
scheme, our DNA procedure allows for a selective and node-adaptive aggregation
of neighboring embeddings of potentially differing locality. In order to avoid
overfitting, we propose to control the channel-wise connections between input
and output by making use of grouped linear projections. In a number of
transductive node-classification experiments, we demonstrate the effectiveness
of our approach
Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs
Graph Neural Networks (GNNs) have shown excellent performance on graphs that
exhibit strong homophily with respect to the node labels i.e. connected nodes
have same labels. However, they perform poorly on heterophilic graphs. Recent
approaches have typically modified aggregation schemes, designed adaptive graph
filters, etc. to address this limitation. In spite of this, the performance on
heterophilic graphs can still be poor. We propose a simple alternative method
that exploits Truncated Singular Value Decomposition (TSVD) of topological
structure and node features. Our approach achieves up to ~30% improvement in
performance over state-of-the-art methods on heterophilic graphs. This work is
an early investigation into methods that differ from aggregation based
approaches. Our experimental results suggest that it might be important to
explore other alternatives to aggregation methods for heterophilic setting.Comment: Accepted at Deep Learning on Graphs: Method and Applications (DLG-KDD
2021
Lean Algebraic Multigrid (LAMG): Fast Graph Laplacian Linear Solver
Laplacian matrices of graphs arise in large-scale computational applications
such as machine learning; spectral clustering of images, genetic data and web
pages; transportation network flows; electrical resistor circuits; and elliptic
partial differential equations discretized on unstructured grids with finite
elements. A Lean Algebraic Multigrid (LAMG) solver of the linear system Ax=b is
presented, where A is a graph Laplacian. LAMG's run time and storage are linear
in the number of graph edges. LAMG consists of a setup phase, in which a
sequence of increasingly-coarser Laplacian systems is constructed, and an
iterative solve phase using multigrid cycles. General graphs pose algorithmic
challenges not encountered in traditional applications of algebraic multigrid.
LAMG combines a lean piecewise-constant interpolation, judicious node
aggregation based on a new node proximity definition, and an energy correction
of the coarse-level systems. This results in fast convergence and substantial
overhead and memory savings. A serial LAMG implementation scaled linearly for a
diverse set of 1666 real-world graphs with up to six million edges. This
multilevel methodology can be fully parallelized and extended to eigenvalue
problems and other graph computations.Comment: 28 page
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
Deep convolutional neural networks (CNN) have recently been shown to generate
promising results for aesthetics assessment. However, the performance of these
deep CNN methods is often compromised by the constraint that the neural network
only takes the fixed-size input. To accommodate this requirement, input images
need to be transformed via cropping, warping, or padding, which often alter
image composition, reduce image resolution, or cause image distortion. Thus the
aesthetics of the original images is impaired because of potential loss of fine
grained details and holistic image layout. However, such fine grained details
and holistic image layout is critical for evaluating an image's aesthetics. In
this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional
Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This
novel scheme is able to accept arbitrary sized images, and learn from both
fined grained details and holistic image layout simultaneously. To enable
training on these hybrid inputs, we extend the method by developing a dedicated
double-subnet neural network structure, i.e. a Multi-Patch subnet and a
Layout-Aware subnet. We further construct an aggregation layer to effectively
combine the hybrid features from these two subnets. Extensive experiments on
the large-scale aesthetics assessment benchmark (AVA) demonstrate significant
performance improvement over the state-of-the-art in photo aesthetic
assessment
EAGr: Supporting Continuous Ego-centric Aggregate Queries over Large Dynamic Graphs
In this work, we present EAGr, a system for supporting large numbers of
continuous neighborhood-based ("ego-centric") aggregate queries over large,
highly dynamic, and rapidly evolving graphs. Examples of such queries include
computation of personalized, tailored trends in social networks, anomaly/event
detection in financial transaction networks, local search and alerts in
spatio-temporal networks, to name a few. Key challenges in supporting such
continuous queries include high update rates typically seen in these
situations, large numbers of queries that need to be executed simultaneously,
and stringent low latency requirements. We propose a flexible, general, and
extensible in-memory framework for executing different types of ego-centric
aggregate queries over large dynamic graphs with low latencies. Our framework
is built around the notion of an aggregation overlay graph, a pre-compiled data
structure that encodes the computations to be performed when an update/query is
received. The overlay graph enables sharing of partial aggregates across
multiple ego-centric queries (corresponding to the nodes in the graph), and
also allows partial pre-computation of the aggregates to minimize the query
latencies. We present several highly scalable techniques for constructing an
overlay graph given an aggregation function, and also design incremental
algorithms for handling structural changes to the underlying graph. We also
present an optimal, polynomial-time algorithm for making the pre-computation
decisions given an overlay graph, and evaluate an approach to incrementally
adapt those decisions as the workload changes. Although our approach is
naturally parallelizable, we focus on a single-machine deployment and show that
our techniques can easily handle graphs of size up to 320 million nodes and
edges, and achieve update/query throughputs of over 500K/s using a single,
powerful machine.Comment: 18 pages, 1 table, 14 figure
Adaptable Precomputation for Random Walker Image Segmentation and Registration
The random walker (RW) algorithm is used for both image segmentation and
registration, and possesses several useful properties that make it popular in
medical imaging, such as being globally optimizable, allowing user interaction,
and providing uncertainty information. The RW algorithm defines a weighted
graph over an image and uses the graph's Laplacian matrix to regularize its
solutions. This regularization reduces to solving a large system of equations,
which may be excessively time consuming in some applications, such as when
interacting with a human user. Techniques have been developed that precompute
eigenvectors of a Laplacian offline, after image acquisition but before any
analysis, in order speed up the RW algorithm online, when segmentation or
registration is being performed. However, precomputation requires certain
algorithm parameters be fixed offline, limiting their flexibility. In this
paper, we develop techniques to update the precomputed data online when RW
parameters are altered. Specifically, we dynamically determine the number of
eigenvectors needed for a desired accuracy based on user input, and derive
update equations for the eigenvectors when the edge weights or topology of the
image graph are changed. We present results demonstrating that our techniques
make RW with precomputation much more robust to offline settings while only
sacrificing minimal accuracy.Comment: 9 pages, 8 figure
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
Betweenness centrality (BC) is one of the most used centrality measures for
network analysis, which seeks to describe the importance of nodes in a network
in terms of the fraction of shortest paths that pass through them. It is key to
many valuable applications, including community detection and network
dismantling. Computing BC scores on large networks is computationally
challenging due to high time complexity. Many approximation algorithms have
been proposed to speed up the estimation of BC, which are mainly
sampling-based. However, these methods are still prone to considerable
execution time on large-scale networks, and their results are often exacerbated
when small changes happen to the network structures. In this paper, we focus on
identifying nodes with high BC in a graph, since many application scenarios are
built upon retrieving nodes with top-k BC. Different from previous heuristic
methods, we turn this task into a learning problem and design an
encoder-decoder based framework to resolve the problem. More specifcally, the
encoder leverages the network structure to encode each node into an embedding
vector, which captures the important structural information of the node. The
decoder transforms the embedding vector for each node into a scalar, which
captures the relative rank of this node in terms of BC. We use the pairwise
ranking loss to train the model to identify the orders of nodes regarding their
BC. By training on small-scale networks, the learned model is capable of
assigning relative BC scores to nodes for any unseen networks, and thus
identifying the highly-ranked nodes. Comprehensive experiments on both
synthetic and real-world networks demonstrate that, compared to representative
baselines, our model drastically speeds up the prediction without noticeable
sacrifce in accuracy, and outperforms the state-of-the-art by accuracy on
several large real-world networks.Comment: 10 pages, 4 figures, 8 table
Graph Attribute Aggregation Network with Progressive Margin Folding
Graph convolutional neural networks (GCNNs) have been attracting increasing
research attention due to its great potential in inference over graph
structures. However, insufficient effort has been devoted to the aggregation
methods between different convolution graph layers. In this paper, we introduce
a graph attribute aggregation network (GAAN) architecture. Different from the
conventional pooling operations, a graph-transformation-based aggregation
strategy, progressive margin folding, PMF, is proposed for integrating graph
features. By distinguishing internal and margin elements, we provide an
approach for implementing the folding iteratively. And a mechanism is also
devised for preserving the local structures during progressively folding. In
addition, a hypergraph-based representation is introduced for transferring the
aggregated information between different layers. Our experiments applied to the
public molecule datasets demonstrate that the proposed GAAN outperforms the
existing GCNN models with significant effectiveness
Adaptive Interaction Modeling via Graph Operations Search
Interaction modeling is important for video action analysis. Recently,
several works design specific structures to model interactions in videos.
However, their structures are manually designed and non-adaptive, which require
structures design efforts and more importantly could not model interactions
adaptively. In this paper, we automate the process of structures design to
learn adaptive structures for interaction modeling. We propose to search the
network structures with differentiable architecture search mechanism, which
learns to construct adaptive structures for different videos to facilitate
adaptive interaction modeling. To this end, we first design the search space
with several basic graph operations that explicitly capture different relations
in videos. We experimentally demonstrate that our architecture search framework
learns to construct adaptive interaction modeling structures, which provides
more understanding about the relations between the structures and some
interaction characteristics, and also releases the requirement of structures
design efforts. Additionally, we show that the designed basic graph operations
in the search space are able to model different interactions in videos. The
experiments on two interaction datasets show that our method achieves
competitive performance with state-of-the-arts
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