154 research outputs found
A YBCO RF-squid variable temperature susceptometer and its applications
The Superconducting QUantum Interference Device (SQUID) susceptibility using a high-temperature radio-frequency (rf) SQUID and a normal metal pick-up coil is employed in testing weak magnetization of the sample. The magnetic moment resolution of the device is 1 x 10(exp -6) emu, and that of the susceptibility is 5 x 10(exp -6) emu/cu cm
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Data from many real-world applications can be naturally represented by
multi-view networks where the different views encode different types of
relationships (e.g., friendship, shared interests in music, etc.) between
real-world individuals or entities. There is an urgent need for methods to
obtain low-dimensional, information preserving and typically nonlinear
embeddings of such multi-view networks. However, most of the work on multi-view
learning focuses on data that lack a network structure, and most of the work on
network embeddings has focused primarily on single-view networks. Against this
background, we consider the multi-view network representation learning problem,
i.e., the problem of constructing low-dimensional information preserving
embeddings of multi-view networks. Specifically, we investigate a novel
Generative Adversarial Network (GAN) framework for Multi-View Network
Embedding, namely MEGAN, aimed at preserving the information from the
individual network views, while accounting for connectivity across (and hence
complementarity of and correlations between) different views. The results of
our experiments on two real-world multi-view data sets show that the embeddings
obtained using MEGAN outperform the state-of-the-art methods on node
classification, link prediction and visualization tasks.Comment: Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, IJCAI-1
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) show promising results for
semi-supervised learning tasks on graphs, thus become favorable comparing with
other approaches. Despite the remarkable success of GCNs, it is difficult to
train GCNs with insufficient supervision. When labeled data are limited, the
performance of GCNs becomes unsatisfying for low-degree nodes. While some prior
work analyze successes and failures of GCNs on the entire model level,
profiling GCNs on individual node level is still underexplored.
In this paper, we analyze GCNs in regard to the node degree distribution.
From empirical observation to theoretical proof, we confirm that GCNs are
biased towards nodes with larger degrees with higher accuracy on them, even if
high-degree nodes are underrepresented in most graphs. We further develop a
novel Self-Supervised-Learning Degree-Specific GCN (SL-DSGC) that mitigate the
degree-related biases of GCNs from model and data aspects. Firstly, we propose
a degree-specific GCN layer that captures both discrepancies and similarities
of nodes with different degrees, which reduces the inner model-aspect biases of
GCNs caused by sharing the same parameters with all nodes. Secondly, we design
a self-supervised-learning algorithm that creates pseudo labels with
uncertainty scores on unlabeled nodes with a Bayesian neural network. Pseudo
labels increase the chance of connecting to labeled neighbors for low-degree
nodes, thus reducing the biases of GCNs from the data perspective. Uncertainty
scores are further exploited to weight pseudo labels dynamically in the
stochastic gradient descent for SL-DSGC. Experiments on three benchmark
datasets show SL-DSGC not only outperforms state-of-the-art
self-training/self-supervised-learning GCN methods, but also improves GCN
accuracy dramatically for low-degree nodes.Comment: Accepted to CIKM 202
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various domains,
such as meteorology and traffic. Due to limitations on data collection,
transmission, and storage, real-world MTS data usually contains missing values,
making it infeasible to apply existing MTS forecasting models such as linear
regression and recurrent neural networks. Though many efforts have been devoted
to this problem, most of them solely rely on local dependencies for imputing
missing values, which ignores global temporal dynamics. Local
dependencies/patterns would become less useful when the missing ratio is high,
or the data have consecutive missing values; while exploring global patterns
can alleviate such problems. Thus, jointly modeling local and global temporal
dynamics is very promising for MTS forecasting with missing values. However,
work in this direction is rather limited. Therefore, we study a novel problem
of MTS forecasting with missing values by jointly exploring local and global
temporal dynamics. We propose a new framework LGnet, which leverages memory
network to explore global patterns given estimations from local perspectives.
We further introduce adversarial training to enhance the modeling of global
temporal distribution. Experimental results on real-world datasets show the
effectiveness of LGnet for MTS forecasting with missing values and its
robustness under various missing ratios.Comment: Accepted by AAAI 202
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Graph neural networks (GNNs) are widely used in many applications. However,
their robustness against adversarial attacks is criticized. Prior studies show
that using unnoticeable modifications on graph topology or nodal features can
significantly reduce the performances of GNNs. It is very challenging to design
robust graph neural networks against poisoning attack and several efforts have
been taken. Existing work aims at reducing the negative impact from adversarial
edges only with the poisoned graph, which is sub-optimal since they fail to
discriminate adversarial edges from normal ones. On the other hand, clean
graphs from similar domains as the target poisoned graph are usually available
in the real world. By perturbing these clean graphs, we create supervised
knowledge to train the ability to detect adversarial edges so that the
robustness of GNNs is elevated. However, such potential for clean graphs is
neglected by existing work. To this end, we investigate a novel problem of
improving the robustness of GNNs against poisoning attacks by exploring clean
graphs. Specifically, we propose PA-GNN, which relies on a penalized
aggregation mechanism that directly restrict the negative impact of adversarial
edges by assigning them lower attention coefficients. To optimize PA-GNN for a
poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to
penalize perturbations using clean graphs and their adversarial counterparts,
and transfers such ability to improve the robustness of PA-GNN on the poisoned
graph. Experimental results on four real-world datasets demonstrate the
robustness of PA-GNN against poisoning attacks on graphs. Code and data are
available here: https://github.com/tangxianfeng/PA-GNN.Comment: Accepted by WSDM 2020. Code and data:
https://github.com/tangxianfeng/PA-GN
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
With the rapid growth and prevalence of social network applications (Apps) in
recent years, understanding user engagement has become increasingly important,
to provide useful insights for future App design and development. While several
promising neural modeling approaches were recently pioneered for accurate user
engagement prediction, their black-box designs are unfortunately limited in
model explainability. In this paper, we study a novel problem of explainable
user engagement prediction for social network Apps. First, we propose a
flexible definition of user engagement for various business scenarios, based on
future metric expectations. Next, we design an end-to-end neural framework,
FATE, which incorporates three key factors that we identify to influence user
engagement, namely friendships, user actions, and temporal dynamics to achieve
explainable engagement predictions. FATE is based on a tensor-based graph
neural network (GNN), LSTM and a mixture attention mechanism, which allows for
(a) predictive explanations based on learned weights across different feature
categories, (b) reduced network complexity, and (c) improved performance in
both prediction accuracy and training/inference time. We conduct extensive
experiments on two large-scale datasets from Snapchat, where FATE outperforms
state-of-the-art approaches by error and
runtime reduction. We also evaluate explanations from FATE, showing strong
quantitative and qualitative performance.Comment: Accepted to KDD 2020 Applied Data Science Trac
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