Search · four archives
Search · four archives
17 papers · ranked by Valyu relevance
Sethupathy Parameswaran, Suresh Sundaram, Yuan Fang
Node classification is a fundamental problem in information retrieval with many real-world applications, such as community detection in social networks, grouping articles published online and product categorization in e-commerce. Zero-shot node classification in text-attributed graphs (TAGs) presents a significant…
Sunwoo Kim, Soo Yong Lee, Kyungho Kim, Hyunjin Hwang + 2 more
Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information from neighboring nodes, is commonly employed to encode node features and graph topology. However, excessive reliance on graph convolution…
Xueqi Ma, Xingjun Ma, Sarah Erfani, Danilo P. Mandic + 1 more
Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-ofdistribution (OOD) samples is essential for deploying graph neural networks (GNNs) in open-world scenarios. Existing methods typically treat all OOD samples as a single class, despite real-world…
Jiangnan Zhou, Sheng Zhang, Bing Wu, Qiuming Wang + 5 more
Existing methods for hypergraph node classification usually rely on local message passing and use a unified strategy for topological modeling across hyperedges of different sizes. However, they have two limitations in semi-supervised settings. First, representation learning mainly depends on local neighborhoods, making…
Setareh Rahimi, Stephen Bonner, Avid Afzal, Marta Milo + 2 more
Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise…
Bo Xiao, Wei Yin, Stanisław Drożdż
Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for characterizing the temporal evolution of transaction systems. However…
Safal Thapaliya, Jiatan Huang, Chuxu Zhang
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts, web pages, or product descriptions, large language models (LLMs) can provide low-cost supervision by annotating a small subset of nodes.…
Shubhajit Roy, Anirban Dasgupta
Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \emph{Temporal-first} approaches build per-node temporal embeddings and only afterwards perform spatial aggregation, whereas \emph{Spatial-first} approaches invert this order, feeding the output of a…
Giovanni Donghi, Daniele Zambon, Luca Pasa, Cesare Alippi + 1 more
Catastrophic forgetting is one of the main obstacles for Online Continual Graph Learning (OCGL), where nodes arrive one by one, distribution drifts may occur at any time and offline training on task-specific subgraphs is not feasible. In this work, we explore a surprisingly simple yet highly effective approach for…
Takuto Takahashi, Itsuki Nakayama, Takahiro Mitani, Ryosuke Kikuchi + 2 more
Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node classification in homophilous graphs, where nodes with the same class label tend to be connected. However, their performance remains limited in heterophilous graphs, where…
Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He + 2 more
In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance. Traditional GNNs often struggle in such scenarios, as they tend to overfit to majority classes while underrepresenting minority…
Amin Khodaei, Zahra Pourabbas, Fatemeh Hashem-zadehdizajyekan, Erfan Esmaeili
Title: Highlights 1. • Modeling the structure of various viruses in the form of directed weighted graphs. 2. • Presenting a feature extraction algorithm based on complex networks metrics. 3. • The impact of the eigen-vector, input centrality and output centrality measures of specific nucleotide triplets within genes…
Muhammed Ali Pala, Muhammet Burhan Navdar, Jae-Ho Han
Background/Objectives: Traditional medical image analysis methods often suffer from locality bias, limiting their ability to model long-range contextual relationships between spatially distributed anatomical structures. To overcome this challenge, this study proposes SPX-GNN (Superpixel Explainable Graph Neural…
Authors not listed
Crystal structure prediction (CSP) is a valuable computational technique used to anticipate the likely crystal structures of a compound of interest. These methods have been proven useful in research and development of pharmaceutical solid forms and in guiding the discovery of materials with targeted properties. Despite…
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
Kai Chen, Ting Hu
Deep learning classifiers for medical image analysis typically function as black boxes, disclosing neither the image features underlying their predictions nor the reasoning by which individual decisions are reached. Peripheral blood cell classification exemplifies this challenge: experienced laboratory professionals…
Bo Xiao, Wei Yin, Yang (Jack) Lu
Detecting anomalies in the Bitcoin transaction network is critical for ensuring blockchain security and stability. The network’s heterogeneous structure and dynamic nature, coupled with scarce labeled anomalies, pose significant challenges for traditional graph-based methods. To address these, we propose Bidirectional…