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Found 19 papers, 2 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.
General2Specialized LLMs Translation for E-commerce
Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and legal documents.
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.
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.
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.
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.
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.
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
This is the first study of adversarial attacks on GVFL.
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.
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.
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
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.
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
