69 research outputs found
Detecting Blackholes and Volcanoes in Directed Networks
In this paper, we formulate a novel problem for finding blackhole and volcano
patterns in a large directed graph. Specifically, a blackhole pattern is a
group which is made of a set of nodes in a way such that there are only inlinks
to this group from the rest nodes in the graph. In contrast, a volcano pattern
is a group which only has outlinks to the rest nodes in the graph. Both
patterns can be observed in real world. For instance, in a trading network, a
blackhole pattern may represent a group of traders who are manipulating the
market. In the paper, we first prove that the blackhole mining problem is a
dual problem of finding volcanoes. Therefore, we focus on finding the blackhole
patterns. Along this line, we design two pruning schemes to guide the blackhole
finding process. In the first pruning scheme, we strategically prune the search
space based on a set of pattern-size-independent pruning rules and develop an
iBlackhole algorithm. The second pruning scheme follows a divide-and-conquer
strategy to further exploit the pruning results from the first pruning scheme.
Indeed, a target directed graphs can be divided into several disconnected
subgraphs by the first pruning scheme, and thus the blackhole finding can be
conducted in each disconnected subgraph rather than in a large graph. Based on
these two pruning schemes, we also develop an iBlackhole-DC algorithm. Finally,
experimental results on real-world data show that the iBlackhole-DC algorithm
can be several orders of magnitude faster than the iBlackhole algorithm, which
has a huge computational advantage over a brute-force method.Comment: 18 page
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data
Jointly extracting entity pairs and their relations is challenging when
working on distantly-supervised data with ambiguous or noisy labels. To
mitigate such impact, we propose uncertainty-aware bootstrap learning, which is
motivated by the intuition that the higher uncertainty of an instance, the more
likely the model confidence is inconsistent with the ground truths.
Specifically, we first explore instance-level data uncertainty to create an
initial high-confident examples. Such subset serves as filtering noisy
instances and facilitating the model to converge fast at the early stage.
During bootstrap learning, we propose self-ensembling as a regularizer to
alleviate inter-model uncertainty produced by noisy labels. We further define
probability variance of joint tagging probabilities to estimate inner-model
parametric uncertainty, which is used to select and build up new reliable
training instances for the next iteration. Experimental results on two large
datasets reveal that our approach outperforms existing strong baselines and
related methods.Comment: ACL 2023 main conference short pape
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
The ability to predict city-wide parking availability is crucial for the
successful development of Parking Guidance and Information (PGI) systems.
Indeed, the effective prediction of city-wide parking availability can improve
parking efficiency, help urban planning, and ultimately alleviate city
congestion. However, it is a non-trivial task for predicting citywide parking
availability because of three major challenges: 1) the non-Euclidean spatial
autocorrelation among parking lots, 2) the dynamic temporal autocorrelation
inside of and between parking lots, and 3) the scarcity of information about
real-time parking availability obtained from real-time sensors (e.g., camera,
ultrasonic sensor, and GPS). To this end, we propose Semi-supervised
Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide
parking availability. Specifically, we first propose a hierarchical graph
convolution structure to model non-Euclidean spatial autocorrelation among
parking lots. Along this line, a contextual graph convolution block and a soft
clustering graph convolution block are respectively proposed to capture local
and global spatial dependencies between parking lots. Additionally, we adopt a
recurrent neural network to incorporate dynamic temporal dependencies of
parking lots. Moreover, we propose a parking availability approximation module
to estimate missing real-time parking availabilities from both spatial and
temporal domain. Finally, experiments on two real-world datasets demonstrate
the prediction performance of SHARE outperforms seven state-of-the-art
baselines.Comment: 8 pages, 9 figures, AAAI-202
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
The self-attention mechanism, which equips with a strong capability of
modeling long-range dependencies, is one of the extensively used techniques in
the sequential recommendation field. However, many recent studies represent
that current self-attention based models are low-pass filters and are
inadequate to capture high-frequency information. Furthermore, since the items
in the user behaviors are intertwined with each other, these models are
incomplete to distinguish the inherent periodicity obscured in the time domain.
In this work, we shift the perspective to the frequency domain, and propose a
novel Frequency Enhanced Hybrid Attention Network for Sequential
Recommendation, namely FEARec. In this model, we firstly improve the original
time domain self-attention in the frequency domain with a ramp structure to
make both low-frequency and high-frequency information could be explicitly
learned in our approach. Moreover, we additionally design a similar attention
mechanism via auto-correlation in the frequency domain to capture the periodic
characteristics and fuse the time and frequency level attention in a union
model. Finally, both contrastive learning and frequency regularization are
utilized to ensure that multiple views are aligned in both the time domain and
frequency domain. Extensive experiments conducted on four widely used benchmark
datasets demonstrate that the proposed model performs significantly better than
the state-of-the-art approaches.Comment: 11 pages, 7 figures, The 46th International ACM SIGIR Conference on
Research and Development in Information Retrieva
Disentangled Causal Graph Learning forOnline Unsupervised Root Cause Analysis
The task of root cause analysis (RCA) is to identify the root causes of
system faults/failures by analyzing system monitoring data. Efficient RCA can
greatly accelerate system failure recovery and mitigate system damages or
financial losses. However, previous research has mostly focused on developing
offline RCA algorithms, which often require manually initiating the RCA
process, a significant amount of time and data to train a robust model, and
then being retrained from scratch for a new system fault.
In this paper, we propose CORAL, a novel online RCA framework that can
automatically trigger the RCA process and incrementally update the RCA model.
CORAL consists of Trigger Point Detection, Incremental Disentangled Causal
Graph Learning, and Network Propagation-based Root Cause Localization. The
Trigger Point Detection component aims to detect system state transitions
automatically and in near-real-time. To achieve this, we develop an online
trigger point detection approach based on multivariate singular spectrum
analysis and cumulative sum statistics. To efficiently update the RCA model, we
propose an incremental disentangled causal graph learning approach to decouple
the state-invariant and state-dependent information. After that, CORAL applies
a random walk with restarts to the updated causal graph to accurately identify
root causes. The online RCA process terminates when the causal graph and the
generated root cause list converge. Extensive experiments on three real-world
datasets with case studies demonstrate the effectiveness and superiority of the
proposed framework
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender
systems for its representation learning capability on user and item embeddings.
However, GCN is vulnerable to noisy and incomplete graphs, which are common in
real world, due to its recursive message propagation mechanism. In the
literature, some work propose to remove the feature transformation during
message propagation, but making it unable to effectively capture the graph
structural features. Moreover, they model users and items in the Euclidean
space, which has been demonstrated to have high distortion when modeling
complex graphs, further degrading the capability to capture the graph
structural features and leading to sub-optimal performance. To this end, in
this paper, we propose a simple yet effective Quaternion-based Graph
Convolution Network (QGCN) recommendation model. In the proposed model, we
utilize the hyper-complex Quaternion space to learn user and item
representations and feature transformation to improve both performance and
robustness. Specifically, we first embed all users and items into the
Quaternion space. Then, we introduce the quaternion embedding propagation
layers with quaternion feature transformation to perform message propagation.
Finally, we combine the embeddings generated at each layer with the mean
pooling strategy to obtain the final embeddings for recommendation. Extensive
experiments on three public benchmark datasets demonstrate that our proposed
QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202
Meta-optimized Contrastive Learning for Sequential Recommendation
Contrastive Learning (CL) performances as a rising approach to address the
challenge of sparse and noisy recommendation data. Although having achieved
promising results, most existing CL methods only perform either hand-crafted
data or model augmentation for generating contrastive pairs to find a proper
augmentation operation for different datasets, which makes the model hard to
generalize. Additionally, since insufficient input data may lead the encoder to
learn collapsed embeddings, these CL methods expect a relatively large number
of training data (e.g., large batch size or memory bank) to contrast. However,
not all contrastive pairs are always informative and discriminative enough for
the training processing. Therefore, a more general CL-based recommendation
model called Meta-optimized Contrastive Learning for sequential Recommendation
(MCLRec) is proposed in this work. By applying both data augmentation and
learnable model augmentation operations, this work innovates the standard CL
framework by contrasting data and model augmented views for adaptively
capturing the informative features hidden in stochastic data augmentation.
Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the
model augmenters, which helps to improve the quality of contrastive pairs
without enlarging the amount of input data. Finally, a contrastive
regularization term is considered to encourage the augmentation model to
generate more informative augmented views and avoid too similar contrastive
pairs within the meta updating. The experimental results on commonly used
datasets validate the effectiveness of MCLRec.Comment: 11 Pages,8 figure
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