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Found 33 papers, 12 papers with code
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs
Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field.
CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations.
Graph Signal Processing for Cross-Domain Recommendation
Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem.
On the Feasibility of Fidelity$^-$ for Graph Pruning
As one of popular quantitative metrics to assess the quality of explanation of graph neural networks (GNNs), fidelity measures the output difference after removing unimportant parts of the input graph.
Revisiting Attention Weights as Interpretations of Message-Passing Neural Networks
However, existing studies often use naive calculations to derive attribution scores from attention, and do not take the precise and careful calculation of edge attribution into consideration.
Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
In this paper, we propose Turbo-CF, a GF-based CF method that is both training-free and matrix decomposition-free.
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature.
Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching
The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices.
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together.
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs
Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher, it still comes with a high computational cost from large matrix multiplications during training.
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Although this can be achieved by applying the inverse propagation $\Pi^{-1}$ before distillation from the teacher GNN, it still comes with a high computational cost from large matrix multiplications during training.
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays.
Node Feature Augmentation Vitaminizes Network Alignment
Network alignment (NA) is the task of discovering node correspondences across multiple networks.
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty
In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks.
DATa: Domain Adaptation-Aided Deep Table Detection Using Visual-Lexical Representations
More specifically, after pre-training one of state-of-the-art vision-based models as our backbone network, we re-train our augmented model, consisting of the vision-based model and the multilayer perceptron (MLP) architecture.
FiFo: Fishbone Forwarding in Massive IoT Networks
Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages.
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models.
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems.
META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks
The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks.
Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
Network alignment (NA) is the task of discovering node correspondences across different networks.
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line.
On the Power of Gradual Network Alignment Using Dual-Perception Similarities
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes.
SiReN: Sign-Aware Recommendation Using Graph Neural Networks
In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy.
Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles
To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones.
IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries.
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs.
An Improved Approach for Estimating Social POI Boundaries With Textual Attributes on Social Media
It has been insufficiently explored how to perform density-based clustering by exploiting textual attributes on social media.
DeepNC: Deep Generative Network Completion
In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs.
Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clustering-based CF (CBCF) method using an incentivized/penalized user (IPU) model only with ratings given by users, which is thus easy to implement.
A Personalized Preference Learning Framework for Caching in Mobile Networks
Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences.
Improved Density-Based Spatio--Textual Clustering on Social Media
DBSCAN may not be sufficient when the input data type is heterogeneous in terms of textual description.
DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio--Temporal--Textual Information
To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media.
Community Detection in Partially Observable Social Networks
The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions.
