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Found 23 papers, 8 papers with code
Network Anomaly Traffic Detection via Multi-view Feature Fusion
MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively.
PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations
Our model combines explicit and implicit augmentation strategies for improved learning performance.
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations.
Dual-view Aware Smart Contract Vulnerability Detection for Ethereum
The wide application of Ethereum technology has brought technological innovation to traditional industries.
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
As the number of IoT devices increases, security concerns become more prominent.
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.
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance.
A Unifying Tensor View for Lightweight CNNs
Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition.
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
Lite it fly: An All-Deformable-Butterfly Network
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns.
Neighborhood Homophily-Guided Graph Convolutional Network
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks.
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.
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.
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched.
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
To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques.
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.
PECAN: A Product-Quantized Content Addressable Memory Network
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ).
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.
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.
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.
