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Found 54 papers, 14 papers with code
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments.
Exploring the Impact of Dataset Bias on Dataset Distillation
Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis.
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process.
Deep Learning-Based Frequency Offset Estimation
In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals.
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition
The use of deep learning for radio modulation recognition has become prevalent in recent years.
RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets
Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness.
Can pre-trained models assist in dataset distillation?
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training.
PathMLP: Smooth Path Towards High-order Homophily
However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency.
Subgraph Networks Based Contrastive Learning
In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information.
Clarify Confused Nodes via Separated Learning
A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics and low efficiency.
How does Contrastive Learning Organize Images?
Contrastive learning, a dominant self-supervised technique, emphasizes similarity in representations between augmentations of the same input and dissimilarity for different ones.
Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Furthermore, most methods concentrate on a single attack goal and lack a generalizable adversary to develop distinct attack strategies for diverse goals, thus limiting precise control over victim model behavior in real-world scenarios.
SR-init: An interpretable layer pruning method
Our SR-init method is inspired by the discovery that the accuracy drop due to stochastic re-initialization of layer parameters differs in various layers.
Neighborhood Homophily-based Graph Convolutional Network
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks.
Data Augmentation on Graphs: A Technical Survey
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems.
Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence
Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms.
Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum
With the development of Web 3. 0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency.
GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections
Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively.
SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph
Cross-lingual knowledge graph entity alignment aims to discover the cross-lingual links in the multi-language KGs, which is of great significance to the NLP applications and multi-language KGs fusion.
Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction
In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins.
Improving robustness of language models from a geometry-aware perspective
On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps.
Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition
Data augmentation, as the strategy of expanding dataset, can improve the generalization of the deep learning models and thus improve the accuracy of the models to a certain extent.
Hyperspectral Imaging for cherry tomato
Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world due to its special flavor.
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network
As the default protocol for exchanging routing reachability information on the Internet, the abnormal behavior in traffic of Border Gateway Protocols (BGP) is closely related to Internet anomaly events.
Understanding the Dynamics of DNNs Using Graph Modularity
Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs.
Graph-Based Similarity of Neural Network Representations
Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning.
RGP: Neural Network Pruning through Its Regular Graph Structure
In this paper, we study the graph structure of the neural network, and propose regular graph based pruning (RGP) to perform a one-shot neural network pruning.
Deep Transfer Clustering of Radio Signals
In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals?
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network
Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models.
Adversarial Sample Detection via Channel Pruning
Experiments on CIFAR10 and SVHN show that the FLOPs and size of our generated model are only 24. 46\% and 4. 86\% of the original model.
Adaptive Visibility Graph Neural Network and It's Application in Modulation Classification
In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier.
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts
In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum.
Identity Inference on Blockchain using Graph Neural Network
In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph.
CLPVG: Circular limited penetrable visibility graph as a new network model for time series
Visibility Graph (VG) transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms.
HVAQ: A High-Resolution Vision-Based Air Quality Dataset
The main contribution of this paper is that to the best of our knowledge, it is the first publicly available, high temporal and spatial resolution air quality dataset containing simultaneous point sensor measurements and corresponding images.
MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN
Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring, which focuses on reconstructing bio-impedance distribution inside the human brain using non-intrusive electromagnetic fields.
Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain.
Time-Series Snapshot Network for Partner Recommendation: A Case Study on OSS
In this paper, we introduce time-series snapshot network (TSSN) which is a mixture network to model the interactions among users and developers.
Social and Information Networks
SigNet: A Novel Deep Learning Framework for Radio Signal Classification
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification
Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc.
MGA: Momentum Gradient Attack on Network
The adversarial attack methods based on gradient information can adequately find the perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of the deep learning model based graph embedding algorithms, but it is also easy to fall into a local optimum.
Social and Information Networks
Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria
It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack.
Social and Information Networks Physics and Society
Time-aware Gradient Attack on Dynamic Network Link Prediction
In this work, we present the first study of adversarial attack on dynamic network link prediction (DNLP).
Multiscale Evolutionary Perturbation Attack on Community Detection
In this paper, we formalize this community detection attack problem in three scales, including global attack (macroscale), target community attack (mesoscale) and target node attack (microscale).
Social and Information Networks Physics and Society
Open DNN Box by Power Side-Channel Attack
However, recent studies indicate that they are also vulnerable to adversarial attacks.
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks
Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range.
Unsupervised Euclidean Distance Attack on Network Embedding
Since a large number of downstream network algorithms, such as community detection and node classification, rely on the Euclidean distance between nodes to evaluate the similarity between them in the embedding space, EDA can be considered as a universal attack on a variety of network algorithms.
Social and Information Networks Physics and Society
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
In particular, we use a bipartite network to construct the user-item network, and represent the interactions among users (or items) by the corresponding one-mode projection network.
Subgraph Networks with Application to Structural Feature Space Expansion
In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
Ranked #10 on
Graph Classification
on MUTAG
Can Adversarial Network Attack be Defended?
In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks.
Social and Information Networks Physics and Society
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction
Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science.
GA Based Q-Attack on Community Detection
In particular, we first give two heuristic attack strategies, i. e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively.
Social and Information Networks
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations
This is the first time to study privacy protection on targeted links against link-prediction-based attacks.
Social and Information Networks Physics and Society
Fast Gradient Attack on Network Embedding
Network embedding maps a network into a low-dimensional Euclidean space, and thus facilitate many network analysis tasks, such as node classification, link prediction and community detection etc, by utilizing machine learning methods.
Physics and Society Social and Information Networks
