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Found 37 papers, 16 papers with code
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
Moreover, we show that existing contrastive objective learns the low-frequency component of the augmentation graph and propose a high-frequency component (HFC)-aware contrastive learning objective that makes the learned embeddings more distinctive.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
Based on this, we propose a relative structural entropy-based position encoding and a multi-head attention masking scheme based on multi-layer encoding trees.
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation
Spatial time series imputation is critically important to many real applications such as intelligent transportation and air quality monitoring.
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
Multi-task Adversarial Learning for Semi-supervised Trajectory-User Linking
We first project users and trajectories into the common latent feature space through learning a projection function (generator) to minimize the distance between the user distribution and the trajectory distribution.
Robust Graph Structure Learning via Multiple Statistical Tests
A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges.
Geometric Interaction Augmented Graph Collaborative Filtering
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security.
HousE: Knowledge Graph Embedding with Householder Parameterization
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images.
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network
The current studies generally learn user preferences according to the transitions of items in the user's session sequence.
A Robust and Generalized Framework for Adversarial Graph Embedding
With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.
Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.
Adversarial Directed Graph Embedding
To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.
Decode with Template: Content Preserving Sentiment Transfer
The two major challenges in existing works lie in (1) effectively disentangling the original sentiment from input sentences; and (2) preserving the semantic content while transferring the sentiment.
Interpretable Deep Learning Model for Online Multi-touch Attribution
DeepMTA mainly contains two parts, the phased-LSTMs based conversion prediction model to catch different time intervals, and the additive feature attribution model combined with shaley values.
SOM-based DDoS Defense Mechanism using SDN for the Internet of Things
To effectively tackle the security threats towards the Internet of things, we propose a SOM-based DDoS defense mechanism using software-defined networking (SDN) in this paper.
DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
The source code suggestions provided by current IDEs are mostly dependent on static type learning.
Deep Transfer Learning for Source Code Modeling
A challenging issue of these approaches is that they require training from starch for a different related problem.
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.
Label-Aware Graph Convolutional Networks
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
Deep Learning for Spatio-Temporal Data Mining: A Survey
Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience.
Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
HINT first models multi-sourced texts (e. g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts.
Multi-Hot Compact Network Embedding
Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling
We evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms the state-of-the-art language models and help reduce the vocabulary size up to 24. 93\%.
Graph Convolutional Neural Networks via Motif-based Attention
Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information.
Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data
In this work, we study how to explore multiple data sources to improve the performance of the stock prediction.
