153 research outputs found
A Survey of Mobile Edge Computing in the Industrial Internet
With the advent of a new round of the Industrial Revolution, the Industrial
Internet will carry the convergence of heterogeneous network and the dynamic
reconfiguration of industrial equipment. In order to further provide higher
performance of network capabilities, the Industrial Internet has experienced
unprecedented growth while facing enormous challenges from the actual needs of
industrial networks. The typical scenarios in industrial applications, combined
with the technical advantages of mobile edge computing, are described in view
of the low latency, high bandwidth and high reliability demanded by the
Industrial Internet in the new era. The key technologies of mobile edge
computing for the Industrial Internet have been outlined in this treatise,
whose feasibility and importance are demonstrated by typical industrial
applications that have been deployed. As combined with the development trend of
the Industrial Internet, this paper summarizes the existing work and discusses
the future research direction of key technologies of mobile edge computing for
the Industrial Internet.Comment: 2019 The 7th International Conference on Information, Communication
and Network
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
Graph classification is a crucial task in many real-world multimedia
applications, where graphs can represent various multimedia data types such as
images, videos, and social networks. Previous efforts have applied graph neural
networks (GNNs) in balanced situations where the class distribution is
balanced. However, real-world data typically exhibit long-tailed class
distributions, resulting in a bias towards the head classes when using GNNs and
limited generalization ability over the tail classes. Recent approaches mainly
focus on re-balancing different classes during model training, which fails to
explicitly introduce new knowledge and sacrifices the performance of the head
classes. To address these drawbacks, we propose a novel framework called
Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature
extractor and an unbiased classifier in a decoupled manner. In the feature
extractor training stage, we develop a graph retrieval module to search for
relevant graphs that directly enrich the intra-class diversity for the tail
classes. Moreover, we innovatively optimize a category-centered supervised
contrastive loss to obtain discriminative representations, which is more
suitable for long-tailed scenarios. In the classifier fine-tuning stage, we
balance the classifier weights with two weight regularization techniques, i.e.,
Max-norm and weight decay. Experiments on various popular benchmarks verify the
superiority of the proposed method against state-of-the-art approaches.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202
A Diffusion model for POI recommendation
Next Point-of-Interest (POI) recommendation is a critical task in
location-based services that aim to provide personalized suggestions for the
user's next destination. Previous works on POI recommendation have laid focused
on modeling the user's spatial preference. However, existing works that
leverage spatial information are only based on the aggregation of users'
previous visited positions, which discourages the model from recommending POIs
in novel areas. This trait of position-based methods will harm the model's
performance in many situations. Additionally, incorporating sequential
information into the user's spatial preference remains a challenge. In this
paper, we propose Diff-POI: a Diffusion-based model that samples the user's
spatial preference for the next POI recommendation. Inspired by the wide
application of diffusion algorithm in sampling from distributions, Diff-POI
encodes the user's visiting sequence and spatial character with two
tailor-designed graph encoding modules, followed by a diffusion-based sampling
strategy to explore the user's spatial visiting trends. We leverage the
diffusion process and its reversed form to sample from the posterior
distribution and optimized the corresponding score function. We design a joint
training and inference framework to optimize and evaluate the proposed
Diff-POI. Extensive experiments on four real-world POI recommendation datasets
demonstrate the superiority of our Diff-POI over state-of-the-art baseline
methods. Further ablation and parameter studies on Diff-POI reveal the
functionality and effectiveness of the proposed diffusion-based sampling
strategy for addressing the limitations of existing methods
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks.
Nevertheless, the majority of GNN-based approaches have been examined using
well-annotated benchmark datasets, leading to suboptimal performance in
real-world graph learning scenarios. To bridge this gap, the present paper
investigates the problem of graph transfer learning in the presence of label
noise, which transfers knowledge from a noisy source graph to an unlabeled
target graph. We introduce a novel technique termed Balance Alignment and
Information-aware Examination (ALEX) to address this challenge. ALEX first
employs singular value decomposition to generate different views with crucial
structural semantics, which help provide robust node representations using
graph contrastive learning. To mitigate both label shift and domain shift, we
estimate a prior distribution to build subgraphs with balanced label
distributions. Building on this foundation, an adversarial domain discriminator
is incorporated for the implicit domain alignment of complex multi-modal
distributions. Furthermore, we project node representations into a different
space, optimizing the mutual information between the projected features and
labels. Subsequently, the inconsistency of similarity structures is evaluated
to identify noisy samples with potential overfitting. Comprehensive experiments
on various benchmark datasets substantiate the outstanding superiority of the
proposed ALEX in different settings.Comment: Accepted by the ACM International Conference on Multimedia (MM) 202
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
Collaborative Filtering (CF) is a pivotal research area in recommender
systems that capitalizes on collaborative similarities between users and items
to provide personalized recommendations. With the remarkable achievements of
node embedding-based Graph Neural Networks (GNNs), we explore the upper bounds
of expressiveness inherent to embedding-based methodologies and tackle the
challenges by reframing the CF task as a graph signal processing problem. To
this end, we propose PolyCF, a flexible graph signal filter that leverages
polynomial graph filters to process interaction signals. PolyCF exhibits the
capability to capture spectral features across multiple eigenspaces through a
series of Generalized Gram filters and is able to approximate the optimal
polynomial response function for recovering missing interactions. A graph
optimization objective and a pair-wise ranking objective are jointly used to
optimize the parameters of the convolution kernel. Experiments on three widely
adopted datasets demonstrate the superiority of PolyCF over current
state-of-the-art CF methods. Moreover, comprehensive studies empirically
validate each component's efficacy in the proposed PolyCF
Proof of User Similarity: the Spatial Measurer of Blockchain
Although proof of work (PoW) consensus dominates the current blockchain-based
systems mostly, it has always been criticized for the uneconomic brute-force
calculation. As alternatives, energy-conservation and energy-recycling
mechanisms heaved in sight. In this paper, we propose proof of user similarity
(PoUS), a distinct energy-recycling consensus mechanism, harnessing the
valuable computing power to calculate the similarities of users, and enact the
calculation results into the packing rule. However, the expensive calculation
required in PoUS challenges miners in participating, and may induce plagiarism
and lying risks. To resolve these issues, PoUS embraces the best-effort schema
by allowing miners to compute partially. Besides, a voting mechanism based on
the two-parties computation and Bayesian truth serum is proposed to guarantee
privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes
itself in recycling the computing power back to blockchain since it turns the
resource wastage to facilitate refined cohort analysis of users, serving as the
spatial measurer and enabling a searchable blockchain. We build a prototype of
PoUS and compare its performance with PoW. The results show that PoUS
outperforms PoW in achieving an average TPS improvement of 24.01% and an
average confirmation latency reduction of 43.64%. Besides, PoUS functions well
in mirroring the spatial information of users, with negligible computation time
and communication cost.Comment: 12 pages,10 figure
Kernel-based Substructure Exploration for Next POI Recommendation
Point-of-Interest (POI) recommendation, which benefits from the proliferation
of GPS-enabled devices and location-based social networks (LBSNs), plays an
increasingly important role in recommender systems. It aims to provide users
with the convenience to discover their interested places to visit based on
previous visits and current status. Most existing methods usually merely
leverage recurrent neural networks (RNNs) to explore sequential influences for
recommendation. Despite the effectiveness, these methods not only neglect
topological geographical influences among POIs, but also fail to model
high-order sequential substructures. To tackle the above issues, we propose a
Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which
combines the characteristics of both geographical and sequential influences in
a collaborative way. KBGNN consists of a geographical module and a sequential
module. On the one hand, we construct a geographical graph and leverage a
message passing neural network to capture the topological geographical
influences. On the other hand, we explore high-order sequential substructures
in the user-aware sequential graph using a graph kernel neural network to
capture user preferences. Finally, a consistency learning framework is
introduced to jointly incorporate geographical and sequential information
extracted from two separate graphs. In this way, the two modules effectively
exchange knowledge to mutually enhance each other. Extensive experiments
conducted on two real-world LBSN datasets demonstrate the superior performance
of our proposed method over the state-of-the-arts. Our codes are available at
https://github.com/Fang6ang/KBGNN.Comment: Accepted by the IEEE International Conference on Data Mining (ICDM)
202
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Graph clustering, which learns the node representations for effective cluster
assignments, is a fundamental yet challenging task in data analysis and has
received considerable attention accompanied by graph neural networks in recent
years. However, most existing methods overlook the inherent relational
information among the non-independent and non-identically distributed nodes in
a graph. Due to the lack of exploration of relational attributes, the semantic
information of the graph-structured data fails to be fully exploited which
leads to poor clustering performance. In this paper, we propose a novel
self-supervised deep graph clustering method named Relational Redundancy-Free
Graph Clustering (RFGC) to tackle the problem. It extracts the attribute-
and structure-level relational information from both global and local views
based on an autoencoder and a graph autoencoder. To obtain effective
representations of the semantic information, we preserve the consistent
relation among augmented nodes, whereas the redundant relation is further
reduced for learning discriminative embeddings. In addition, a simple yet valid
strategy is utilized to alleviate the over-smoothing issue. Extensive
experiments are performed on widely used benchmark datasets to validate the
superiority of our RFGC over state-of-the-art baselines. Our codes are
available at https://github.com/yisiyu95/R2FGC.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems
(TNNLS 2024
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