Tootfinder

Fair Graph Neural Network with Supervised Contrastive Regularization
Mahdi Tavassoli Kejani (UT3), Fadi Dornaika (IMT), Jean-Michel Loubes (IMT)
https://arxiv.org/abs/2404.06090 <…

Fair Graph Neural Network with Supervised Contrastive Regularization
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. However, challenges arise from biases that can be hidden not only in the node attributes but also in the connections between entities. Therefore, ensuring fairness in graph neural network learning has become a critical problem. To address this issue, we propose a novel model for training fairness-aware GNN, which enhances…

Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Yiwei Zou, Ting Li, Zong-fu Luo
https://arxiv.org/abs/2403.04977 …

Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis, providing essential reference for discerning the significance of nodes within complex networks. These measures find wide applications in critical tasks, such as community detection and network dismantling. However, their practical implementation on extensive networks remains computationally demanding due to their high time complexity. To mitigate these computational challenges, numerous approximat…

Profile of Vulnerability Remediations in Dependencies Using Graph Analysis
Fernando Vera, Palina Pauliuchenka, Ethan Oh, Bai Chien Kao, Louis DiValentin, David A. Bader
https://arxiv.org/abs/2403.04989

Profile of Vulnerability Remediations in Dependencies Using Graph Analysis
This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) to the critical challenge of open source package vulnerability remediation by analyzing control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Our approach uniquely applies node centrality metrics -- degree, norm, and closeness centrality -- to the GAT model, enabling a detailed examination of package…

This https://arxiv.org/abs/2211.03102 has been replaced.
link: https://scholar.google.com/scholar?q=a

Collaborative Video Analytics on Distributed Edges with Multiagent Deep Reinforcement Learning
Deep Neural Network (DNN) based video analytics empowers many computer vision-based applications to achieve high recognition accuracy. To reduce inference delay and bandwidth cost for video analytics, the DNN models can be deployed on the edge nodes, which are proximal to end users. However, the processing capacity of an edge node is limited, potentially incurring substantial delay if the inference requests on an edge node is overloaded. While efforts have been made to enhance video analytics b…

This https://arxiv.org/abs/2307.04384 has been replaced.
link: https://scholar.google.com/scholar?q=a

Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn user and item embeddings with Graph Convolutional Network (GCN) and utilize these embeddings for CF models. However, existing GCN-based methods are insufficient in generating satisfactory embeddings for CF models. This is because they fail to model complex node dependencies and v…

This https://arxiv.org/abs/2211.03102 has been replaced.
link: https://scholar.google.com/scholar?q=a

Collaborative Video Analytics on Distributed Edges with Multiagent Deep Reinforcement Learning
Deep Neural Network (DNN) based video analytics empowers many computer vision-based applications to achieve high recognition accuracy. To reduce inference delay and bandwidth cost for video analytics, the DNN models can be deployed on the edge nodes, which are proximal to end users. However, the processing capacity of an edge node is limited, potentially incurring substantial delay if the inference requests on an edge node is overloaded. While efforts have been made to enhance video analytics b…

This https://arxiv.org/abs/2404.10800 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Advancing Network Intrusion Detection: Integrating Graph Neural Networks with Scattering Transform and Node2Vec for Enhanced Anomaly Detection
In this paper, we present two novel methods in Network Intrusion Detection Systems (NIDS) using Graph Neural Networks (GNNs). The first approach, Scattering Transform with E-GraphSAGE (STEG), utilizes the scattering transform to conduct multi-resolution analysis of edge feature vectors. This provides a detailed representation that is essential for identifying subtle anomalies in network traffic. The second approach improves node representation by initiating with Node2Vec, diverging from standar…

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang
https://arxiv.org/abs/2402.14708

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{\underline{Ca}}usal \textbf{\underline{T}}emporal \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{N}etwork (CaT-GNN), which leverages causal invariant learning to reveal inherent cor…

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Yankai Chen, Yixiang Fang, Qiongyan Wang, Xin Cao, Irwin King
https://arxiv.org/abs/2402.12411

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Node importance estimation problem has been studied conventionally with homogeneous network topology analysis. To deal with network heterogeneity, a few recent methods employ graph neural models to automatically learn diverse sources of information. However, the major concern revolves around that their full adaptive learning process may lead to insufficient information exploration, thereby formulating the problem as the isolated node value prediction with underperformance and less interpretabil…

Timely Communications for Remote Inference
Md Kamran Chowdhury Shisher, Yin Sun, I-Hong Hou
https://arxiv.org/abs/2404.16281 https://…

Timely Communications for Remote Inference
In this paper, we analyze the impact of data freshness on remote inference systems, where a pre-trained neural network infers a time-varying target (e.g., the locations of vehicles and pedestrians) based on features (e.g., video frames) observed at a sensing node (e.g., a camera). One might expect that the performance of a remote inference system degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang, Piotr Luczynski, Saleh Ashkboos, Florian Scheidl, Armon Carigiet, Chio Ge, Nabil Abubaker, Maciej Besta, Tal Ben-Nun, Torsten Hoefler
https://arxiv.org/abs/2402.19364<…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

Graph Neural Network for Crawling Target Nodes in Social Networks
Kirill Lukyanov, Mikhail Drobyshevskiy, Danil Shaikhelislamov, Denis Turdakov
https://arxiv.org/abs/2403.13865

Graph Neural Network for Crawling Target Nodes in Social Networks
Social networks crawling is in the focus of active research the last years. One of the challenging task is to collect target nodes in an initially unknown graph given a budget of crawling steps. Predicting a node property based on its partially known neighbourhood is at the heart of a successful crawler. In this paper we adopt graph neural networks for this purpose and show they are competitive to traditional classifiers and are better for individual cases. Additionally we suggest a training sa…

This https://arxiv.org/abs/2402.16240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
We investigate node representation learning on text-attributed graphs (TAGs), where nodes are associated with text information. Although recent studies on graph neural networks (GNNs) and pretrained language models (PLMs) have exhibited their power in encoding network and text signals, respectively, less attention has been paid to delicately coupling these two types of models on TAGs. Specifically, existing GNNs rarely model text in each node in a contextualized way; existing PLMs can hardly be…

This https://arxiv.org/abs/2402.19364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

Deep Overlapping Community Search via Subspace Embedding
Qing Sima, Jianke Yu, Xiaoyang Wang, Wenjie Zhang, Ying Zhang, Xuemin Lin
https://arxiv.org/abs/2404.14692

Deep Overlapping Community Search via Subspace Embedding
Community search (CS) aims to identify a set of nodes based on a specified query, leveraging structural cohesiveness and attribute homogeneity. This task enjoys various applications, ranging from fraud detection to recommender systems. In contrast to algorithm-based approaches, graph neural network (GNN) based methods define communities using ground truth labels, leveraging prior knowledge to explore patterns from graph structures and node features. However, existing solutions face three major …

This https://arxiv.org/abs/2402.19364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix in…

Distinguishing Neighborhood Representations Through Reverse Process of GNNs for Heterophilic Graphs
MoonJeong Park, Jaeseung Heo, Dongwoo Kim
https://arxiv.org/abs/2403.10543

Distinguishing Neighborhood Representations Through Reverse Process of GNNs for Heterophilic Graphs
Graph Neural Network (GNN) resembles the diffusion process, leading to the over-smoothing of learned representations when stacking many layers. Hence, the reverse process of message passing can sharpen the node representations by inverting the forward message propagation. The sharpened representations can help us to better distinguish neighboring nodes with different labels, such as in heterophilic graphs. In this work, we apply the design principle of the reverse process to the three variants …