159 research outputs found
Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases
Many studies have been conducted on seeking the efficient solution for
subgraph similarity search over certain (deterministic) graphs due to its wide
application in many fields, including bioinformatics, social network analysis,
and Resource Description Framework (RDF) data management. All these works
assume that the underlying data are certain. However, in reality, graphs are
often noisy and uncertain due to various factors, such as errors in data
extraction, inconsistencies in data integration, and privacy preserving
purposes. Therefore, in this paper, we study subgraph similarity search on
large probabilistic graph databases. Different from previous works assuming
that edges in an uncertain graph are independent of each other, we study the
uncertain graphs where edges' occurrences are correlated. We formally prove
that subgraph similarity search over probabilistic graphs is #P-complete, thus,
we employ a filter-and-verify framework to speed up the search. In the
filtering phase,we develop tight lower and upper bounds of subgraph similarity
probability based on a probabilistic matrix index, PMI. PMI is composed of
discriminative subgraph features associated with tight lower and upper bounds
of subgraph isomorphism probability. Based on PMI, we can sort out a large
number of probabilistic graphs and maximize the pruning capability. During the
verification phase, we develop an efficient sampling algorithm to validate the
remaining candidates. The efficiency of our proposed solutions has been
verified through extensive experiments.Comment: VLDB201
Energy-Efficient β
As the first priority of query processing in wireless sensor networks is to save the limited energy of sensor nodes and in many sensing applications a part of skyline result is enough for the user’s requirement, calculating the exact skyline is not energy-efficient relatively. Therefore, a new approximate skyline query, β-approximate skyline query which is limited by a
guaranteed error bound, is proposed in this paper. With an objective to reduce the communication cost in evaluating
β-approximate skyline queries, we also propose an energy-efficient processing algorithm using mapping and filtering
strategies, named Actual Approximate Skyline (AAS). And more than that, an extended algorithm named Hypothetical Approximate Skyline (HAS) which replaces the real tuples with the hypothetical ones is proposed to further reduce the communication cost. Extensive experiments on synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Knowledge distillation (KD) has demonstrated its effectiveness to boost the
performance of graph neural networks (GNNs), where its goal is to distill
knowledge from a deeper teacher GNN into a shallower student GNN. However, it
is actually difficult to train a satisfactory teacher GNN due to the well-known
over-parametrized and over-smoothing issues, leading to invalid knowledge
transfer in practical applications. In this paper, we propose the first
Free-direction Knowledge Distillation framework via Reinforcement learning for
GNNs, called FreeKD, which is no longer required to provide a deeper
well-optimized teacher GNN. The core idea of our work is to collaboratively
build two shallower GNNs in an effort to exchange knowledge between them via
reinforcement learning in a hierarchical way. As we observe that one typical
GNN model often has better and worse performances at different nodes during
training, we devise a dynamic and free-direction knowledge transfer strategy
that consists of two levels of actions: 1) node-level action determines the
directions of knowledge transfer between the corresponding nodes of two
networks; and then 2) structure-level action determines which of the local
structures generated by the node-level actions to be propagated. In essence,
our FreeKD is a general and principled framework which can be naturally
compatible with GNNs of different architectures. Extensive experiments on five
benchmark datasets demonstrate our FreeKD outperforms two base GNNs in a large
margin, and shows its efficacy to various GNNs. More surprisingly, our FreeKD
has comparable or even better performance than traditional KD algorithms that
distill knowledge from a deeper and stronger teacher GNN.Comment: Accepted to KDD 202
Learning to Generate Parameters of ConvNets for Unseen Image Data
Typical Convolutional Neural Networks (ConvNets) depend heavily on large
amounts of image data and resort to an iterative optimization algorithm (e.g.,
SGD or Adam) to learn network parameters, which makes training very time- and
resource-intensive. In this paper, we propose a new training paradigm and
formulate the parameter learning of ConvNets into a prediction task: given a
ConvNet architecture, we observe there exists correlations between image
datasets and their corresponding optimal network parameters, and explore if we
can learn a hyper-mapping between them to capture the relations, such that we
can directly predict the parameters of the network for an image dataset never
seen during the training phase. To do this, we put forward a new hypernetwork
based model, called PudNet, which intends to learn a mapping between datasets
and their corresponding network parameters, and then predicts parameters for
unseen data with only a single forward propagation. Moreover, our model
benefits from a series of adaptive hyper recurrent units sharing weights to
capture the dependencies of parameters among different network layers.
Extensive experiments demonstrate that our proposed method achieves good
efficacy for unseen image datasets on two kinds of settings: Intra-dataset
prediction and Inter-dataset prediction. Our PudNet can also well scale up to
large-scale datasets, e.g., ImageNet-1K. It takes 8967 GPU seconds to train
ResNet-18 on the ImageNet-1K using GC from scratch and obtain a top-5 accuracy
of 44.65 %. However, our PudNet costs only 3.89 GPU seconds to predict the
network parameters of ResNet-18 achieving comparable performance (44.92 %),
more than 2,300 times faster than the traditional training paradigm
Scalable Algorithms for Laplacian Pseudo-inverse Computation
The pseudo-inverse of a graph Laplacian matrix, denoted as , finds
extensive application in various graph analysis tasks. Notable examples include
the calculation of electrical closeness centrality, determination of Kemeny's
constant, and evaluation of resistance distance. However, existing algorithms
for computing are often computationally expensive when dealing with
large graphs. To overcome this challenge, we propose novel solutions for
approximating by establishing a connection with the inverse of a
Laplacian submatrix . This submatrix is obtained by removing the -th
row and column from the original Laplacian matrix . The key advantage of
this connection is that exhibits various interesting combinatorial
interpretations. We present two innovative interpretations of based
on spanning trees and loop-erased random walks, which allow us to develop
efficient sampling algorithms. Building upon these new theoretical insights, we
propose two novel algorithms for efficiently approximating both electrical
closeness centrality and Kemeny's constant. We extensively evaluate the
performance of our algorithms on five real-life datasets. The results
demonstrate that our novel approaches significantly outperform the
state-of-the-art methods by several orders of magnitude in terms of both
running time and estimation errors for these two graph analysis tasks. To
further illustrate the effectiveness of electrical closeness centrality and
Kemeny's constant, we present two case studies that showcase the practical
applications of these metrics
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Graph structured data often possess dynamic characters in nature, e.g., the
addition of links and nodes, in many real-world applications. Recent years have
witnessed the increasing attentions paid to dynamic graph neural networks for
modelling such graph data, where almost all the existing approaches assume that
when a new link is built, the embeddings of the neighbor nodes should be
updated by learning the temporal dynamics to propagate new information.
However, such approaches suffer from the limitation that if the node introduced
by a new connection contains noisy information, propagating its knowledge to
other nodes is not reliable and even leads to the collapse of the model. In
this paper, we propose AdaNet: a robust knowledge Adaptation framework via
reinforcement learning for dynamic graph neural Networks. In contrast to
previous approaches immediately updating the embeddings of the neighbor nodes
once adding a new link, AdaNet attempts to adaptively determine which nodes
should be updated because of the new link involved. Considering that the
decision whether to update the embedding of one neighbor node will have great
impact on other neighbor nodes, we thus formulate the selection of node update
as a sequence decision problem, and address this problem via reinforcement
learning. By this means, we can adaptively propagate knowledge to other nodes
for learning robust node embedding representations. To the best of our
knowledge, our approach constitutes the first attempt to explore robust
knowledge adaptation via reinforcement learning for dynamic graph neural
networks. Extensive experiments on three benchmark datasets demonstrate that
AdaNet achieves the state-of-the-art performance. In addition, we perform the
experiments by adding different degrees of noise into the dataset,
quantitatively and qualitatively illustrating the robustness of AdaNet.Comment: 14 pages, 6 figure
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