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Found 16 papers, 9 papers with code
Causal Learning for Trustworthy Recommender Systems: A Survey
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
A new class of methods have been proposed to tackle this problem by aggregating path information.
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
Can Directed Graph Neural Networks be Adversarially Robust?
This work presents the first investigation into the robustness of GNNs in the context of directed graphs, aiming to harness the profound trust implications offered by directed graphs to bolster the robustness and resilience of GNNs.
Heterogeneous Social Event Detection via Hyperbolic Graph Representations
This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space.
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades.
Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.
DAGAD: Data Augmentation for Graph Anomaly Detection
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
Survey on Applications of Neurosymbolic Artificial Intelligence
In recent years, the Neurosymbolic framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance.
Syntax Matters! Syntax-Controlled in Text Style Transfer
Existing text style transfer (TST) methods rely on style classifiers to disentangle the text's content and style attributes for text style transfer.
Text Style Transfer: A Review and Experimental Evaluation
This article aims to provide a comprehensive review of recent research efforts on text style transfer.
Graph Convolutional Networks with EigenPooling
To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.
Ranked #1 on
Graph Classification
on NC1
Multi-dimensional Graph Convolutional Networks
Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.
Social and Information Networks
Learning Deep Network Representations with Adversarially Regularized Autoencoders
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.
Using link and content over time for embedding generation in Dynamic Attributed Networks
In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks.
Outlier Detection for Text Data : An Extended Version
In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data.
